IEEE 2018 / 19 - Cloud Computing Projects

IEEE 2018: Stability of Evolving Fuzzy Systems based on Data
Abstract: Evolving fuzzy systems (EFSs) are now well develope and widely used thanks to their ability to self-adapt both their structures and parameters online. Since the concept was firstly introduced two decades ago, many different types of EFSs have been successfully implemented. However, there are only very few works considering the stability of the EFSs, and thesestudies were limited to certain types of membership functions with specifically pre-defined parameters, which largely increases the complexity of the learning process. At the same time, stability analysis is of paramount importance for control applications and provides the theoretical guarantees for the convergence of the learning algorithms. In this paper, we introduce the stability proofof a class of EFSs based on data clouds, which are grounded at the AnYa type fuzzy systems and the recently introduced empirical data analysis (EDA) methodological framework. By employing data clouds, the class of EFSs of AnYa type considered in this work avoids the traditional way of defining membership functions for each input variable in an explicit manner and its learning process is entirely data-driven. The stability of the considered EFS of AnYa type is proven through the Lyapunov theory, and the proof of stability shows that the average identification error converges to a small neighborhood of zero. Although, the stability proof presented in this paper is specially elaborated for the considered EFS, it is also applicable to general EFSs. The proposed method is illustrated with Box-Jenkins gas furnace problem, one nonlinear system identification problem, Mackey-Glass time series prediction problem, eight real-world benchmark regression problems as well as a high frequency trading prediction problem. Compared with other EFSs, the numerical examples show that the considered EFS in this paper provides guaranteed stability
as well as a better approximation accuracy.

IEEE 2018: Anonymous and Traceable Group Data Sharing in Cloud Computing

 Abstract: Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plaintexts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that are able to perform expressive keyword search are computationally inefficient sinc they are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies (i.e., predicates, access structures) to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and achieves significant performance improvement over existing schemes. We formally define its security, and prove that it is selectively secure in the standard model. Also, we implement the proposed scheme using a rapid prototyping tool called Charm [37], and conduct several experiments to evaluate it performance. The results demonstrate that our scheme is much more efficient than the ones built over the composite-order groups.

IEEE 2018: Efficient and Expressive Keyword Search Over Encrypted Data in Cloud

Abstract: Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plaintexts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that are able to perform expressive keyword search are computationally inefficient sinc they are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies (i.e., predicates, access structures) to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and achieves significant performance improvement over existing schemes. We formally define its security, and prove that it is selectively secure in the standard model. Also, we implement the proposed scheme using a rapid prototyping tool called Charm [37], and conduct several experiments to evaluate it performance. The results demonstrate that our scheme is much more efficient than the ones built over the composite-order groups.

IEEE 2017: Two-Factor Data Access Control With Efficient Revocation for Multi-Authority Cloud Storage Systems

Abstract: Attribute-based encryption, especially for ciphertext-policy attribute-based encryption, can fulfill the functionality of fine-grained access control in cloud storage systems. Since users' attributes may be issued by multiple attribute authorities, multi-authority ciphertext-policy attribute-based encryption is an emerging cryptographic primitive for enforcing attribute-based access control on outsourced data. However, most of the existing multi-authority attribute-based systems are either insecure in attribute-level revocation or lack of efficiency in communication overhead and computation cost. In this paper, we propose anattribute-based access control scheme with two-factor protection for multi-authority cloud storage systems. In our proposed scheme, any user can recover the outsourced data if and only if this user holds sufficient attribute secret keys with respect to the access policy and authorization key in regard to the outsourced data. In addition, the proposed scheme enjoys the properties of constant-size ciphertext and small computation cost. Besides supporting the attribute-level revocation, our proposed scheme allows data owner to carry out the user-level revocation. The security analysis, performance comparisons, and experimental results indicate that our proposed scheme is not only secure but also practical.Read More

IEEE 2017: FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data

Abstract: Spatial data have wide applications, e.g., location-based services, and geometric range queries (i.e., finding points inside geometric areas, e.g., circles or polygons) are one of the fundamental search functions over spatial data. The rising demand of outsourcing data is moving large-scale datasets, including large-scale spatial datasets, to public clouds. Meanwhile, due to the concern of insider attackers and hackers on public clouds, the privacy of spatial datasets should be cautiously preserved while querying them at the server side, especially for location-based and medical usage. In this paper, we formalize the concept of Geometrically Searchable Encryption, and propose an efficient scheme, named FastGeo, to protect the privacy of clients’ spatial datasets stored and queried at a public server. With FastGeo, which is a novel two-level search for encrypted spatial data, an honest-but-curious server can efficiently perform geometric range queries, and correctly return data points that are inside a geometric range to a client without learning sensitive data points or this private query. FastGeo supports arbitrary geometric areas, achieves sublinear search time, and enables dynamic updates over encrypted spatial datasets.Read More

IEEE 2017: Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories

Abstract:Storage requirements for visual data have been increasing in recent years, following the emergence of many highly interactive multimedia services and applications for mobile devices in both personal and corporate scenarios. This has been a key driving factor for the adoption of cloud-based data outsourcing solutions. However, outsourcing data storage to the Cloud also leads to new security challenges that must be carefully addressed, especially regarding privacy. In this paper we propose a secure framework for outsourced privacy-preserving storage and retrieval in large shared image repositories. Our proposal is based on IES-CBIR, a novel Image Encryption Scheme that exhibits Content-Based Image Retrieval properties. The framework enables both encrypted storage and searching using Content-Based Image Retrieval queries while preserving privacy against honest-but-curious cloud administrators. We have built a prototype of the proposed framework, formally analyzed and proven its security properties, and experimentally evaluated its performance and retrieval precision. Read More

IEEE 2017: Temporal Task Scheduling With Constrained Service Delay for Profit Maximization in Hybrid Clouds

Abstract : As cloud computing is becoming growingly popular, consumers’ tasks around the world arrive in cloud data centers. A private cloud provider aims to achieve profit maximization by intelligently scheduling tasks while guaranteeing the service delay bound of delay-tolerant tasks. However, the aperiodicity of arrival tasks brings a challenging problem of how to dynamically schedule all arrival tasks given the fact that the capacity of a private cloud provider is limited. Previous works usually provide an admission control to intelligently refuse some of arrival tasks. Nevertheless, this will decrease the throughput of a private cloud, and cause revenue loss. This paper studies the problem of how to maximize the profit of a private cloud in hybrid clouds while guaranteeing the service delay bound of delay-tolerant tasks. We propose a profit maximization algorithm (PMA) to discover the temporal variation of prices in hybrid clouds. The temporal task scheduling provided by PMA can dynamically schedule all arrival tasks to execute in private and public clouds. The sub problem in each iteration of PMA is solved by the proposed hybrid heuristic optimization algorithm, simulated annealing particle swarm optimization (SAPSO). Besides, SAPSO is compared with existing baseline algorithms. Extensive simulation experiments demonstrate that the proposed method can greatly increase the throughput and the profit of a private cloud while guaranteeing the service delay bound.Read More

IEEE 2017: Optimizing Cloud-Service Performance: Efficient Resource Provisioning via Optimal Workload Allocation

Abstract: Abstract—Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers’ requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this paper a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines. Two cloudservice performance metrics are mathematically characterized, namely the percentile and the mean of the stochastic response time of a customer request, in closed form. Based upon the proposed multi-tenant framework, a workload allocation algorithm, termed maxmin- cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is also given. Furthermore, the resource-provisioning problem in the cloud is also studied in light of the max-min-cloud algorithm. In particular, an efficient resource-provisioning strategy is proposed for serving dynamically arriving customer requests. These findings can be used by businesses to build a better understanding of how much virtual resource in the cloud they may need to

meet customers’ expectations subject to cost constraintsRead more


IEEE 2017: Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks

Abstract: Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the endusers, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.Read More



IEEE 2017: Optimizing Green Energy, Cost, and Availability in Distributed Data Centers

Abstract: Integrating renewable energy and ensuring high availability are among two major requirements for geo distributed data centers. Availability is ensured by provisioning spare capacity across the data centers to mask data center failures (either partial or complete). We propose a mixed integer linear programming formulation for capacity planning while minimizing the total cost of ownership (TCO) for highly available, green, distributed data centers. We minimize the cost due to power consumption and server deployment, while targeting a minimum usage of green energy. Solving our model shows that capacity provisioning considering green energy integration, not only lowers carbon footprint but also reduces the TCO. Results show that up to 40% green energy usage is feasible with marginal increase in the TCO compared to the other cost-aware models.Read More

IEEE 2017: Cost Minimization Algorithms for Data Center Management

Abstract: Due to the increasing usage of cloud computing applications, it is important to minimize energy cost consumed by a data center, and simultaneously, to improve quality of service via data center management. One promising approach is to switch some servers in  a data center to the idle mode for saving energy while to keep a suitable number of servers in the active mode for providing timely service. In this paper, we design both online and offline algorithms for this problem. For the offline algorithm, we formulate data center management as a cost minimization problem by considering energy cost, delay cost (to measure service quality), and switching cost (to change servers’ active/idle mode). Then, we analyze certain properties of an optimal solution which lead to a dynamic programming based algorithm. Moreover, by revising the solution procedure, we successfully eliminate the recursive procedure and achieve an optimal offline algorithm with a polynomial complexity.Read More

IEEE 2017: Vehicular Cloud Data Collection for Intelligent Transportation Systems
Abstract: The Internet of Things (IoT) envisions to connect billions of sensors to the Internet, in order to provide new applications and services for smart cities. IoT will allow the evolution of the Internet of Vehicles (IoV) from existing Vehicular Ad hoc Networks (VANETs), in which the delivery of various services will be offered to drivers by integrating vehicles, sensors, and mobile devices into a global network. To serve VANET with computational resources, Vehicular Cloud Computing (VCC) is recently envisioned with the objective of providing traffic solutions to improve our daily driving. These solutions involve applications and services for the benefit of Intelligent Transportation Systems (ITS), which represent an important part of IoV. Data collection is an important aspect in ITS, which can effectively serve online travel systems with the aid of Vehicular Cloud (VC). In this paper, we involve the new paradigm of VCC to propose a data collection model for the benefit of ITS. We show via simulation results that the participation of low percentage of vehicles in a dynamic VC is sufficient to provide meaningful data collection.Read More

IEEE 2017: RAAC: Robust and Auditable Access Control with Multiple Attribute Authorities for Public Cloud Storage

Abstract: Data access control is a challenging issue in public cloud storage systems. Ciphertext-Policy Attribute-Based En-cryption (CP-ABE) has been adopted as a promising technique to provide flexible, fine-grained and secure data access control for cloud storage with honest-but-curious cloud servers. However, in the existing CP-ABE schemes, the single attribute authority must execute the time-consuming user legitimacy verification and secret key distribution, and hence it results in a single-point performance bottleneck when a CP-ABE scheme is adopted in a large-scale cloud storage system. Users may be stuck in the waiting queue for a long period to obtain their secret keys, thereby resulting in low-efficiency of the system. Although multi-authority access control schemes have been proposed, these schemes still cannot overcome the drawbacks of single-point bottleneck and low efficiency, due to the fact that each of the authorities still independently manages a disjoint attribute set.Read More


IEEE 2017: Privacy-Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing

Abstract: Privacy has become a considerable issue when the applications of big data are dramatically growing in cloud computing. The benefits of the implementation for these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The execution time of the data encryption is one of the serious issues during the data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level companioning with privacy concerns. In this paper, we concentrate on privacy and propose a novel data encryption approach, which is called Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data and use privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements.Read More


IEEE 2017: Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage

Abstract: Remote data integrity checking (RDIC) enables a data storage server, says a cloud server, to prove to a verifier that it is actually storing a data owner’s data honestly. To date, a number of RDIC protocols have been proposed in the literature, but most of the constructions suffer from the issue of a complex key management, that is, they rely on the expensive public key infrastructure (PKI), which might hinder the deployment of RDIC in practice. In this paper, we propose a new construction of identity-based (ID-based) RDIC protocol by making use of key- homomorphic cryptographic primitive to reduce the system complexity and the cost for establishing and managing the public key authentication framework in PKI-based RDIC schemes. We formalize ID-based RDIC and its security model, including security against a malicious cloud server and zero knowledge privacy against a third party verifier. The proposed ID-based RDIC protocol leaks no information of the stored data to the verifier during the RDIC process. The new construction is proven secure against the malicious server in the generic group model and achieves zero knowledge privacy against a verifier. Extensive security analysis and implementation results demonstrate that the proposed protocol is provably secure and practical in the real-world applications. Read More


IEEE 2017: Identity-Based Data Outsourcing with Comprehensive Auditing in Clouds

Abstract: Cloud storage system provides facilitative file storage and sharing services for distributed clients. To address integrity, controllable outsourcing and origin auditing concerns on outsourced files, we propose an identity-based data outsourcing (IBDO) scheme equipped with desirable features advantageous over existing proposals in securing outsourced data. First, our IBDO scheme allows a user to authorize dedicated proxies to upload data to the cloud storage server on her behalf, e.g., a company may authorize some employees to upload files to the company’s cloud account in a controlled way. The proxies are identified and authorized with their recognizable identities, which eliminates complicated certificate management in usual secure distributed computing systems. Second, our IBDO scheme facilitates comprehensive auditing, i.e., our scheme not only permits regular integrity auditing as in existing schemes for securing outsourced data, but also allows to audit the information on data origin, type and consistence of outsourced files.Read More

IEEE 2017: TAFC: Time and Attribute Factors Combined Access Control on Time-Sensitive Data in Public Cloud
Abstract: The new paradigm of outsourcing data to the cloud is a double-edged sword. On one side, it frees up data owners from the technical management, and is easier for the data owners to share their data with intended recipients when data are stored in the cloud. On the other side, it brings about new challenges about privacy and security protection. To protect data confidentiality against the honest-but-curious cloud service provider, numerous works have been proposed to support fine-grained data access control. However, till now, no efficient schemes can provide the scenario of fine-grained access control together with the capacity of time-sensitive data publishing. In this paper, by embedding the mechanism of timed-release encryption into CP-ABE (Ciphertext- Policy Attribute-based Encryption), we propose TAFC: a new time and attribute factors combined access control on time sensitive data stored in cloud. Extensive security and performance analysis shows that our proposed scheme is highly efficient and
satisfies the security requirements for time-sensitive data storage in public cloudRead More

IEEE 2017: Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Abstract: Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure deduplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data deduplication systems, our system has two advantages. Firstly, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Secondly, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion.

IEEE 2017: A Collision-Mitigation Cuckoo Hashing Scheme for Large-scale Storage Systems

Abstract: With the rapid growth of the amount of information, cloud computing servers need to process and analyze large amounts of high-dimensional and unstructured data timely and accurately. This usually requires many query operations. Due to simplicity and ease of use, cuckoo hashing schemes have been widely used in real-world cloud-related applications. However due to the potential hash collisions, the cuckoo hashing suffers from endless loops and high insertion latency, even high risks of re-construction of entire hash table. In order to address these problems, we propose a cost-efficient cuckoo hashing scheme, called MinCounter. The idea behind MinCounter is to alleviate the occurrence of endless loops in the data insertion by selecting unbusy kicking-out routes. MinCounter selects the “cold” (infrequently accessed), rather than random, buckets to handle hash collisions. We further improve the concurrency of the MinCounter scheme to pursue higher performance and adapt to concurrent applications. MinCounter has the salient features of offering efficient insertion and query services and delivering high performance of cloud servers, as well as enhancing the experiences for cloud users. We have implemented MinCounter in a large-scale cloud test bed and examined the performance by using three real-world traces. Extensive experimental results demonstrate the efficacy and efficiency of MinCounter.Read More

IEEE 2016: Reducing Fragmentation for In-line Deduplication Backup Storage via Exploiting Backup History and Cache Knowledge

Abstract: In backup systems, the chunks of each backup are physically scattered after deduplication, which causes a challenging fragmentation problem. We observe that the fragmentation comes into sparse and out-of-order containers. The sparse container decreases restore performance and garbage collection efficiency, while the out-of-order container decreases restore performance if the restore cache is small. In order to reduce the fragmentation, we propose History-Aware Rewriting algorithm (HAR) and Cache-Aware Filter (CAF). HAR exploits historical information in backup systems to accurately identify and reduce sparse containers, and CAF exploits restore cache knowledge to identify the out-of-order containers that hurt restore performance. CAF efficiently complements HAR in datasets where out-of-order containers are dominant. To reduce the metadata overhead of the garbage collection, we further propose a Container-Marker Algorithm (CMA) to identify valid containers instead of valid chunks. Our extensive experimental results from real-world datasets show HAR significantly improves the restore performance by 2.84-175.36 × at a cost of only rewriting 0.5-2.03 percent data.

IEEE 2016: Secure Data Sharing in Cloud Computing Using Revocable-Storage Identity-Based Encryption

Abstract: Cloud computing provides a flexible and convenient way for data sharing, which brings various benefits for both the society and individuals. But there exists a natural resistance for users to directly outsource the shared data to the cloud server since the data often contain valuable information. Thus, it is necessary to place cryptographically enhanced access control on the shared data. Identity-based encryption is a promising cryptographical primitive to build a practical data sharing system. However, access control is not static. That is, when some user’s authorization is expired, there should be a mechanism that can remove him/her from the system. Consequently, the revoked user cannot access both the previously and subsequently shared data. To this end, we propose a notion called revocable-storage identity-based encryption (RS-IBE), which can provide the forward/backward security of cipher text by introducing the functionalities of user revocation and cipher text update simultaneously. Furthermore, we present a concrete construction of RS-IBE, and prove its security in the defined security model. The performance comparisons indicate that the proposed RS-IBE scheme has advantages in terms of functionality and efficiency, and thus is feasible for a practical and cost-effective data-sharing system. Finally, we provide implementation results of the proposed scheme to demonstrate its practicability.

IEEE 2016: Key-Aggregate Searchable Encryption (KASE) for Group Data Sharing via Cloud Storage

Abstract: The capability of selectively sharing encrypted data with different users via public cloud storage may greatly ease security concerns over inadvertent data leaks in the cloud. A key challenge to designing such encryption schemes lies in the efficient management of encryption keys. The desired flexibility of sharing any group of selected documents with any group of users demands different encryption keys to be used for different documents. However, this also implies the necessity of securely distributing to users a large number of keys for both encryption and search, and those users will have to securely store the received keys, and submit an equally large number of keyword trapdoors to the cloud in order to perform search over the shared data. The implied need for secure communication, storage, and complexity clearly renders the approach impractical. In this paper, we address this practical problem, which is largely neglected in the literature, by proposing the novel concept of key-aggregate search able encryption and instantiating the concept through a concrete KASE scheme, in which a data owner only needs to distribute a single key to a user for sharing a large number of documents, and the user only needs to submit a single trapdoor to the cloud for querying the shared documents. The security analysis and performance evaluation both confirm that our proposed schemes are provably secure and practically efficient.

IEEE 2016: Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

Abstract: The advent of the cloud computing makes storage outsourcing becomes a rising trend, which promotes the secure remote data auditing a hot topic that appeared in the research literature. Recently some research considers the problem of secure and efficient public data integrity auditing for shared dynamic data. However, these schemes are still not secure against the collusion of cloud storage server and revoked group users during user revocation in practical cloud storage system. In this paper, we figure out the collusion attack in the exiting scheme and provide an efficient public integrity auditing scheme with secure group user revocation based on vector commitment and verifier-local revocation group signature. We design a concrete scheme based on the our scheme definition. Our scheme supports the public checking and efficient user revocation and also some nice properties, such as confidently, efficiency, countability and traceability of secure group user revocation. Finally, the security and experimental analysis show that, compared with its relevant schemes our scheme is also secure and efficient.


IEEE 2016: Secure Auditing and Duplicating Data in Cloud

Abstract: As the cloud computing technology develops during the last decade outsourcing data to cloud service for storage becomes an attractive trend, which benefits in sparing efforts on heavy data maintenance and management. Nevertheless, since the outsourced cloud storage is not fully trustworthy, it raises security concerns on how to realize data deduplication in cloud while achieving integrity auditing. In this work, we study the problem of integrity auditing and secure deduplication on cloud data. Specifically, aiming at achieving both data integrity and deduplication in cloud, we propose two secure systems, namely Sec Cloud and Sec Cloud . Sec Cloud introduces an auditing entity with a maintenance of a Map Reduce cloud, which helps clients generate data tags before uploading as well as audit the integrity of data having been stored in cloud. Compared with previous work, the computation by user in Sec Cloud greatly reduced during the file uploading and auditing phases. Sec Cloud is designed motivated by the fact that customers always want to encrypt their data before uploading, and enables integrity auditing and secure deduplication on encrypted data.


IEEE 2016: Fog Computing May Help to Save Energy in Cloud Computing           
IEEE 2016  Cloud Computing
Abstract—Tiny computers located in end-user premises are becoming popular as local servers for Internet of Things (IoT) and Fog computing services. These highly distributed servers that can host and distribute content and applications in a peer-to-peer (P2P) fashion are known as nano data centers (nDCs). Despite the growing popularity of nano servers, their energy consumption is not well-investigated. To study energy consumption of nDCs, we propose and use flow-based and time-based energy consumption models for shared and unshared network equipment, respectively. To apply and validate these models, a set of measurements and experiments are performed to compare energy consumption of a service provided by nDCs and centralized data centers (DCs). A number of findings emerge from our study, including the factors in the system design that allow nDCs to consume less energy than its centralized counterpart. These include the type of access network attached to nano servers and nano server’s time utilization (the ratio of the idle time to active time). Additionally, the type of applications running on nDCs and factors such as number of downloads, number of updates, and amount of preloaded copies of data influence the energy cost. Our results reveal that number of hops between a user and content has little impact in the total energy consumption compared to the above-mentioned factors. We show that nano servers in Fog computing can complement centralized DCs to serve certain applications, mostly IoT applications for which the source of data is in end-user premises, and lead to energy saving if the applications (or a part of them) are off-loadable from centralized DCs and run on nDCs       

IEEE 2016: CloudArmor: Supporting Reputation-based Trust Management for Cloud Services     
IEEE 2016  Cloud Computing 
 Abstract—Trust management is one of the most challenging issues for the adoption and growth of cloud computing. The highly dynamic, distributed, and non-transparent nature of cloud services introduces several challenging issues such as privacy, security, and availability. Preserving consumers’ privacy is not an easy task due to the sensitive information involved in the interactions between consumers and the trust management service. Protecting cloud services against their malicious users (e.g., such users might give misleading feedback to disadvantage a particular cloud service) is a difficult problem. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of cloud environments. In this article, we describe the design and implementation of CloudArmor, a reputation-based trust management framework that provides a set of functionalities to deliver Trust as a Service (TaaS), which includes i) a novel protocol to prove the credibility of trust feedbacks and preserve users’ privacy, ii) an adaptive and robust credibility model for measuring the credibility of trust feedbacks to protect cloud services from malicious users and to compare the trustworthiness of cloud services, and iii) an availability model to manage the availability of the decentralized implementation of the trust management service. The feasibility and benefits of our approach have been validated by a prototype and experimental studies using a collection of real-world trust feedbacks on cloud services.
IEEE 2016: Secure Optimization Computation Outsourcing in Cloud Computing:&nbsp A Case Study of Linear Programming
IEEE 2016  Cloud Computing
Abstract—Cloud computing enables an economically promising paradigm of computation outsourcing. However, how to protect customers confidential data processed and generated during the computation is becoming the major security concern. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. Our mechanism design explicitly decomposes LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computation than the general circuit representation. Specifically, by formulating private LP problem as a set of matrices/vectors, we develop efficient privacy-preserving problem transformation techniques, which allow customers to transform the original LP into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP and derive the necessary and sufficient conditions that correct results must satisfy. Such result verification mechanism is very efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design.

IEEE 2016: Ensures Dynamic access and Secure E-Governance system in Clouds Services – EDSE
IEEE 2016 Cloud Computing
Abstract— E-Governance process helps the public to learn the information and available of data’s themselves rather than being dependent on a physical guidance. They have been driven through e-govern experience over the past decade; hence there is a necessity to explore new E-Governance concepts with advanced technologies. These systems are now exposed to wide numbers of threat while handling theinformation. This paper therefore designing an efficient system for ensuring security and dynamic operation, so Remote Integrity and secure dynamic operation is designed and implemented in E-Governance environment. The data is stored in the server using dynamic data operation with proposed method which enables the user to access the data for further usage. Here the system does an authentication process to prevent the data loss and ensuring security with reliability method. An efficient distributed storage auditing mechanism is planned which overcomes the limitations in handling the data loss. The content was made easy through the means of cloud computing by using innovative method during information retrieval. Ensuring data security in this service enforces error localization and easy identification of misbehaving server. Availability, Confidentiality and integrity are the key factors of the security. In nature the data are dynamic in cloud service; hence this process aims to process the operation with reduced computational rate, space and time consumption. And also ensure trust based secured access control.

IEEE 2016: On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
IEEE 2016 Cloud Computing

Abstract— The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs, they ignore the network traffic generated in the shuffle phase, which plays a critical role in performance enhancement. Traditionally, a hash function is used to partition intermediate data among reduce tasks, which, however, is not traffic-efficient because network topology and data size associated with each key are not taken into consideration. In this paper, we study to reduce network traffic cost for a MapReduce job by designing a novel intermediate data partition scheme. Furthermore, we jointly consider the aggregator placement problem, where each aggregator can reduce merged traffic from multiple map tasks. A decomposition-based distributed algorithm is proposed to deal with the large-scale optimization problem for big data application and an online algorithm is also designed to adjust data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost under both offline and online cases.

IEEE 2016: A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data
IEEE 2016 Cloud Computing
Abstract— Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF_IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results . Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly.Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

IEEE 2016: An Efficient Privacy-Preserving Ranked Keyword  Search Method
IEEE 2016 Cloud Computing
Abstract— Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.

IEEE 2016: Differentially Private Online Learning for Cloud-Based Video Recommendation with Multimedia Big Data in Social Networks
IEEE 2016 Cloud Computing
Abstract With the rapid growth in multimedia services and the enormous offers of video contents in online social networks,users have difficulty in obtaining their interests. Therefore, various personalized recommendation systems have been proposed. However, they ignore that the accelerated proliferation of social media data has led to the big data era, which has greatly impeded the process of video recommendation. In addition, none of them has considered both the privacy of users’ contexts (e,g., social status, ages and hobbies) and video service vendors’ repositories, which are extremely sensitive and of significant commercial value. To handle the problems, we propose a cloud-assisted differentially private video recommendation system based on distributed online learning. In our framework, service vendorsare modeled as distributed cooperative learners, recommending videos according to user’s context, while simultaneously adapting the video-selection strategy based on user-click feedback to maximize total user clicks (reward). Considering the sparsity and heterogeneity of big social media data, we also propose a novel geometric differentially private model, which can greatly reduce the performance (recommendation accuracy) loss. Our simulation shows the proposed algorithms outperform other existing methods and keep a delicate balance between computing accuracy and privacy preserving level.

IEEE 2016: Fine-Grained Two-Factor Access Control for Web-Based Cloud Computing Services
IEEE 2016 Cloud Computing

AbstractIn this paper, we introduce a new fine-grained two-factor authentication (2FA) access control system for web-based cloud computing services. Specifically, in our proposed 2FA access control system, an attribute-based access control  mechanism is implemented with the necessity of both a user secret key and a lightweight security device. As a user cannot access the system if they do not hold both, the mechanism can enhance the security of the system, especially in those scenarios where many users share the same computer for web-based cloud services. In addition, attribute-based control in the system also enables the cloud server to restrict the access to those users with the same set of attributes while preserving user privacy, i.e., the cloud server only knows that the user fulfills the required predicate, but has no idea on the exact identity of the user. Finally, we also carry out a simulation to demonstrate the practicability of our proposed 2FA system.

IEEE 2016: Dual-Server Public-Key Encryption with Keyword Search for Secure Cloud Storage
IEEE 2016 Cloud Computing

AbstractSearchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this work, we investigate the security of a well-known cryptographic primitive, namely Public Key Encryption with Keyword Search (PEKS) which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside Keyword Guessing Attack (KGA) launched by the malicious server. To address this security vulnerability, we propose a new PEKS framework named Dual-Server Public Key Encryption with Keyword Search (DS-PEKS). As another main contribution, we define a new variant of the Smooth Projective Hash Functions (SPHFs) referred to as linear and homomorphic SPHF (LH-SPHF). We then show a generic construction of secure DS-PEKS from LH-SPHF. To illustrate the feasibility of our new framework, we provide an efficient instantiation of the general framework from a DDH-based LH-SPHF and show that it can achieve the strong security against inside KGA.

IEEE 2016: DeyPoS: Deduplicatable Dynamic Proof of Storage for Multi-User Environments
IEEE 2016 Cloud Computing
AbstractDynamic Proof of Storage (PoS) is a useful cryptographic primitive that enables a user to check the integrity of out sourced files and to efficiently update the files in a cloud server. Although researchers have proposed many dynamic PoS schemes in single user environments, the problem in multi-user environments has not been investigated sufficiently. A practical multi-user cloud storage system needs the secure client-side cross-user deduplication technique, which allows a user to skip the uploading process and obtain the ownership of the files immediately, when other owners of the same files have uploaded them to the cloud server. To the best of our knowledge, none of the existing dynamic PoSs can support this technique. In this paper, we introduce the concept of deduplicatable dynamic proof of storage and propose an efficient construction called DeyPoS, to achieve dynamic PoS and secure cross-user deduplication, simultaneously. Considering the challenges of structure diversity and private tag generation, we exploit a novel tool called Homomorphic Authenticated Tree (HAT). We prove the security of our construction, and the theoretical analysis and experimental results show that our construction is efficient in practice.

IEEE 2016:  KSF-OABE: Outsourced Attribute-Based Encryption with Keyword Search Function for Cloud Storage
IEEE 2016 Cloud Computing
Abstract — Cloud computing becomes increasingly popular for data owners to outsource their data to public cloud servers while allowing intended data users to retrieve these data stored in cloud. This kind of computing model brings challenges to the security and privacy of data stored in cloud. Attribute-based encryption (ABE) technology has been used to design fine-grained access control system, which provides one good method to solve the security issues in cloud setting. However, the computation cost and ciphertext size in most ABE schemes grow with the complexity of the access policy. Outsourced ABE(OABE) with fine-grained access control system can largely reduce the computation cost for users who want to access encrypted data stored in cloud by outsourcing the heavy computation to cloud service provider (CSP). However, as the amount of encrypted files stored in cloud is becoming very huge, which will hinder efficient query processing. To deal with above problem, we present a new cryptographic primitive called attribute-based encryption scheme with outsourcing key-issuing and outsourcing decryption, which can implement keyword search function (KSF-OABE). The proposed KSF-OABE scheme is proved secure against chosen-plaintext attack (CPA). CSP performs partial decryption task delegated by data user without knowing anything about the plaintext. Moreover, the CSP can perform encrypted keyword search without knowing anything about the keywords embedded in trapdoor.

IEEE 2016:  SecRBAC: Secure data in the Clouds
IEEE 2016 Cloud Computing

Abstract —Most current security solutions are based on perimeter security. However, Cloud computing breaks the organization perimeters. When data resides in the Cloud, they reside outside the organizational bounds. This leads users to a loos of control over their data and raises reasonable security concerns that slow down the adoption of Cloud computing. Is the Cloud service provider accessing the data? Is it legitimately applying the access control policy defined by the user? This paper presents a data-centric access control solution with enriched role-based expressiveness in which security is focused on protecting user data regardless the Cloud service provider that holds it. Novel identity-based and proxy re-encryption techniques are used to protect the authorization model. Data is encrypted and authorization rules are cryptographically protected to preserve user data against the service provider access or misbehavior. The authorization model provides high expressiveness with role hierarchy and resource hierarchy support. The solution takes advantage of the logic formalism provided by Semantic Web technologies, which enables advanced rule management like semantic conflict detection. A proof of concept implementation has been developed and a working prototypical deployment of the proposal has been integrated within Google services.

IEEE 2015:  Identity-based Encryption with Outsourced Revocation in Cloud Computing
IEEE 2015 Cloud Computing
Abstract — Identity-Based Encryption (IBE) which simplifies the public key and certificate management at Public Key Infrastructure (PKI) is an important alternative to public key encryption. However, one of the main efficiency drawbacks of IBE is the overhead computation at Private Key Generator (PKG) during user revocation. Efficient revocation has been well studied in traditional PKI setting, but the cumbersome management of certificates is precisely the burden that IBE strives to alleviate. In this paper, aiming at tackling the critical issue of identity revocation, we introduce outsourcing computation into IBE for the first time and propose a revocable IBE scheme in the server-aided setting. Our scheme offloads most of the key generation related operations during key-issuing and key-update processes to a Key Update Cloud Service Provider, leaving only a constant number of simple operations for PKG and users to perform locally. This goal is achieved by utilizing a novel collusion-resistant technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound the identity component and the time component. Furthermore, we propose another construction which is provable secure under the recently formulized Refereed Delegation of Computation model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

IEEE 2015: Control Cloud Data Access Privilege and Anonymity With Fully Anonymous Attribute-Based Encryption
IEEE 2015 Cloud Computing

Abstract — Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it.Various schemes based on the attribute-based encryption have been proposed to secure the cloud storage. However, most work focuses on the data contents privacy and the access control, while less attention is paid to the privilege control and the identity privacy. In this paper, we present a semianonymous privilege control scheme AnonyControl to address not only the data privacy, but also the user identity privacy in existing access control schemes. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semianonymity. Besides, it also generalizes the file access control to the privilege control, by which privileges of all operations on the cloud data can be managed in a fine-grained manner. Subsequently, we present the AnonyControl-F, which fully prevents the identity leakage and achieve the full anonymity. Our security analysis shows that both AnonyControl and AnonyControl-F are secure under the decisional bilinear Diffie–Hellman assumption, and our performance evaluation exhibits the feasibility of our schemes.

IEEE 2015: DROPS: Division and Replication of Data in Cloud for Optimal Performance and Security
IEEE 2015 Cloud Computing

Abstract —Outsourcing data to a third-party administrative control, as is done in cloud computing, gives rise to security concerns. The data compromise may occur due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. However, the employed security strategy must also take into account the optimization of the data retrieval time. In this paper, we propose Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that collectively approaches the security and performance issues. In the DROPS methodology, we divide a file into fragments, and replicate the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack, no meaningful information is revealed to the attacker. Moreover, the nodes storing the fragments, are separated with certain distance by means of graph T-coloring to prohibit an attacker of guessing the locations of the fragments. Furthermore, the DROPS methodology does not rely on the traditional cryptographic techniques for the data security; thereby relieving the system of computationally expensive methodologies. We show that the probability to locate and compromise all of the nodes storing the fragments of a single file is extremely low. We also compare the performance of the DROPS methodology with ten other schemes. The higher level of security with slight performance overhead was observed.

IEEE 2015: An Efficient Green Control Algorithm in Cloud Computing for Cost Optimization
IEEE 2015 Cloud Computing
Abstract —Cloud computing is a new paradigm for delivering remote computing resources through a network. However, achieving an energy-efficiency control and simultaneously satisfying a performance guarantee have become critical issues for cloud providers. In this paper, three power-saving policies are implemented in cloud systems to mitigate server idle power. The challenges of controlling service rates and applying the N-policy to optimize operational cost within a performance guarantee are first studied. A cost function has been developed in which the costs of power consumption, system congestion and server startup are all taken into consideration. The effect of energy-efficiency controls on response times, operating modes and incurred costs are all demonstrated. Our objectives are to find the optimal service rate and mode-switching restriction, so as to minimize cost within a response time guarantee under varying arrival rates. An efficient green control (EGC) algorithm is first proposed for solving constrained optimization problems and making costs/performances tradeoffs in systems with different power-saving policies. Simulation results show that the benefits of reducing operational costs and improving response times can be verified by applying the power-saving policies combined with the proposed algorithm as compared to a typical system under a same performance guarantee.

IEEE 2015: Revisiting Attribute-Based Encryption With Verifiable Outsourced Decryption
IEEE 2015 Cloud Computing
Abstract — Attribute-based encryption (ABE) is a promising technique for fine-grained access control of encrypted data in a cloud storage, however, decryption involved in the ABEs is usually too expensive for resource-constrained front-end users, which greatly hinders its practical popularity. In order to reduce the decryption overhead for a user to recover the plaintext, Green et al. suggested to outsource the majority of the decryption work without revealing actually data or private keys. To ensure the third-party service honestly computes the outsourced work, Lai et al. provided a requirement of verifiability to the decryption of ABE, but their scheme doubled the size of the underlying ABE ciphertext and the computation costs. Roughly speaking, their main idea is to use a parallel encryption technique, while one of the encryption components is used for the verification purpose. Hence, the bandwidth and the computation cost are doubled. In this paper, we investigate the same problem. In particular, we propose a more efficient and generic construction of ABE with verifiable outsourced decryption based on an attribute based key encapsulation mechanism, a symmetric-key encryption scheme and a commitment scheme. Then, we prove the security and the verification soundness of our constructed ABE scheme in the standard model. Finally, we instantiate our scheme with concrete building blocks. Compared with Lai et al.’s scheme, our scheme reduces the bandwidth and the computation costs almost by half.

IEEE 2015: I-sieve: An inline high performance deduplication system used in cloud storage
Abstract: Data deduplication is an emerging and widely employed method for current storage systems. As this technology is gradually applied in inline scenarios such as with virtual machines and cloud storage systems, this study proposes a novel deduplication architecture called I-sieve. The goal of I-sieve is to realize a high performance data sieve system based on iSCSI in the cloud storage system. We also design the corresponding index and mapping tables and present a multi-level cache using a solid state drive to reduce RAM consumption and to optimize lookup performance. A prototype of I-sieve is implemented based on the open source iSCSI target, and many experiments have been conducted driven by virtual machine images and testing tools. The evaluation results show excellent deduplication and foreground performance. More importantly, I-sieve can co-exist with the existing deduplication systems as long as they support the iSCSI protocol.

IEEE 2015: A Hybrid Cloud Approach for Secure Authorized Deduplication
Abstract: Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. To better protect data security, this paper makes the first attempt to formally address the problem of authorized data deduplication. Different from traditional deduplication systems, the differential privileges of users are further considered in duplicate check besides the data itself. We also present several new deduplication constructions supporting authorized duplicate check in a hybrid cloud architecture. Security analysisdemonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement a prototype of our proposed authorized duplicate check scheme and conduct test bed experiments using our prototype. We show that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations.

IEEE 2015: Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybrid Clouds
Abstract: With the recent advent of cloud computing technologies, a growing number of content distribution applications are contemplating a switch to cloud-based services, for better scalability and lower cost. Two key tasks are involved for such a move: to migrate the contents to cloud storage, and to distribute the Web service load to cloud-based Web services. The main issue is to best utilize the cloud as well as the application provider's existing private cloud, to serve volatile requests with service response time guarantee at all times, while incurring the minimum operational cost. While it may not be too difficult to design a simple heuristic, proposing one with guaranteed cost optimality over a long run of the system constitutes an intimidating challenge. Employing Lyapunov optimization techniques, we design a dynamic control algorithm to optimally place contents and dispatch requests in a hybrid cloud infrastructure spanning geo-distributed data centers, which minimizes overall operational cost overtime, subject to service response time constraints. Rigorous analysis shows that the algorithm nicely bounds the response times within the preset QoS target, and guarantees that the overall cost is within a small constant gap from the optimum achieved by a T-slot look ahead mechanism with known future information. We verify the performance of our dynamic algorithm with prototype-based evaluation.

IEEE 2015: Cloud Sky: A Controllable Data Self-Destruction System for Un trusted Cloud Storage Networks

Abstract: In cloud services, users may frequently be required to reveal their personal private information which could be stored in the cloud to used by different parts for different purposes. However, in a cloud-wide storage network, the servers are easily under strong attacks and also commonly experience software/hardware faults. As such, the private information could be under great risk in such an un trusted environment. Given that the presented personal sensitive information is usually out of user's control in most cloud-based services, ensuring data security and privacy protection with respect to un trusted storage network has become a formidable challenge in research. To address these challenges, in this paper we propose a self-destruction system, named Cloud Sky, which is able to enforce the security of user privacy over the un trusted cloud in a controllable way. Cloud Sky exploits a key control mechanism based on the attribute-based encryption (ABE) and takes advantage of active storage networks to allow the user to control the subjective life-cycle and the access control polices of the private data whose integrity is ensured by using HMAC to cope with un trusted environments and there by adapting it to the cloud in terms of both performance and security requirements. The feasibility of the system in terms of its performance and scalability is demonstrated by experiments on a real large-scale storage network

IEEE 2015: Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem

Abstract: In this paper we introduce an energy-aware operation model used for load balancing and application scaling on a cloud. The basic philosophy of our approach is dening an energy-optimal operation regime and attempting to maximize the number of servers operating in this regime. Idle and lightly-loaded servers are switched to one of the sleep states to save energy. The load balancing and scaling algorithms also exploit some of the most desirable features of server consolidation mechanisms discussed in the literature.

IEEE 2015: SecDep: A user-aware efficient fine-grained secure deduplication scheme with multi-level key management
Abstract:  Nowadays, many customers and enterprises backup their data to cloud storage that performs deduplication to save storage space and network bandwidth. Hence, how to perform secure deduplication becomes a critical challenge for cloud storage. According to our analysis, the state-of-the-art secure deduplication methods are not suitable for cross-user fine grained data deduplication. They either suffer brute-force attacks that can recover files falling into a known set, or incur large computation (time) overheads. Moreover, existing approaches of convergent key management incur large space overheads because of the huge number of chunks shared among users. Our observation that cross-user redundant data are mainly from the duplicate files, motivates us to propose an efficient secure deduplication scheme SecDep. SecDep employs User-Aware Convergent Encryption (UACE) and Multi-Level Key management (MLK) approaches. (1) UACE combines cross-user file-level and inside-user chunk-level deduplication, and exploits different secure policies among and inside users to minimize the computation overheads. Specifically, both of file-level and chunk-level deduplication use variants of Convergent Encryption (CE) to resist brute-force attacks. The major difference is that the file-level CE keys are generated by using a server-aided method to ensure security of cross-user deduplication, while the chunk-level keys are generated by using a user-aided method with lower computation overheads. (2) To reduce key space overheads, MLK uses file-level key to encrypt chunk-level keys so that the key space will not increase with the number of sharing users. Furthermore, MLK splits the file-level keys into share-level keys and distributes them to multiple key servers to ensure security and reliability of file-level keys. Our security analysis demonstrates that SecDep ensures data confidentiality and key security. Our experiment results based on several large real-world datasets show that SecDep is more time efficient and key-space-efficient than the state-of-the-art secure deduplication approaches.

IEEE 2015:A Secure Client Side De duplication Scheme in Cloud Storage Environments
IEEE 2015 Cloud Computing
Abstract—recent years have witnessed the trend of leveraging cloud-based services for large scale content storage, processing, and distribution. Security and privacy are among top concerns for the public cloud environments. Towards these security challenges, we propose and implement, on Open Stack Swift, a new client-side deduplication scheme for securely storing and sharing outsourced data via the public cloud. The originality of our proposal is twofold. First, it ensures better confidentiality towards unauthorized users. That is, every client computes a per data key to encrypt the data that he intends to store in the cloud. As such, the data access is managed by the data owner. Second, by integrating access rights in metadata file, an authorized user can decipher an encrypted file only with his private key.

 IEEE 2015 :Adaptive Algorithm for Minimizing Cloud Task Length with Prediction Errors

Abstract—compared to traditional distributed computing like Grid system, it is non-trivial to optimize cloud task’s execution Performance due to its more constraints like user payment budget and divisible resource demand. In this paper, we analyze in-depth our proposed optimal algorithm minimizing task execution length with divisible resources and payment budget: (1) We derive the upper bound of cloud task length, by taking into account both workload prediction errors and host load prediction errors. With such state-of the-art bounds, the worst-case task execution performance is predictable, which can improve the Quality of Service in turn. (2) We design a dynamic version for the algorithm to adapt to the load dynamics over task execution progress, further improving the resource utilization. (3)We rigorously build a cloud prototype over a real cluster environment with 56 virtual machines, and evaluate our algorithm with different levels of resource contention. Cloud users in our cloud system are able to compose various tasks based on off-the-shelf web services. Experiments show that task execution lengths under our algorithm are always close to their theoretical optimal values, even in a competitive situation with limited available resources. We also observe a high level of fair treatment on the resource allocation among all tasks.

 An Efficient Information Retrieval Approach for Collaborative Cloud Computing

Abstract—the collaborative cloud computing (CCC) which is collaboratively supported by various organizations (Google, IBM, AMAZON, MICROSOFT) offers a promising future for information retrieval. Human beings tend to keep things simple by moving the complex aspects to computing. As a consequence, we prefer to go to one or a limited number of sources for all our information needs. In contemporary scenario where information is replicated, modified (value added), and scattered geographically; retrieving information in a suitable form requires lot more effort from the user and thus difficult. For instance, we would like to go directly to the source of information and at the same time not to be burdened with additional effort. This is where, we can make use of learning systems (Neural Network based) that can intelligently decide and retrieve the information that we need by going directly to the source of information. This also, reduces single point of failure and eliminates bottlenecks in the path of information flow, Reduces the Time delay and it provide remarkable ability to overcome from traffic conjection complicated patterns. It makes Efficient information retrieval approach for collaborative cloud computing. both secure and verifiable, without relying on random oracles. Finally, we show an implementation of our

Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation          

Abstract—With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.

Compatibility-aware Cloud Service Composition under Fuzzy Preferences of Users

 Abstract—When a single Cloud service (i.e., a software image and a virtual machine), on its own, cannot satisfy all the user requirements, a composition of Cloud services is required. Cloud service composition, which includes several tasks such as discovery, compatibility checking, selection, and deployment, is a complex process and users find it difficult to select the best one among the hundreds, if not thousands, of possible compositions available. Service composition in Cloud raises even new challenges caused by diversity of users with different expertise requiring their applications to be deployed across difference geographical locations with distinct legal constraints. The main difficulty lies in selecting a combination of virtual appliances (software images) and infrastructure services that are compatible and satisfy a user with vague preferences. Therefore, we  Present a framework and algorithms which simplify Cloud service composition for unskilled users. We develop an ontology based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge. In addition, to minimize effort of users in expressing their preferences, we apply combination of evolutionary algorithms and fuzzy logic for composition optimization. This lets users express their needs in linguistics terms which brings a great comfort to them compared to systems that force users to assign exact weights for all preferences.

Consistency as a Service: Auditing Cloud Consistency

Abstract—Cloud storage services have become commercially popular due to their overwhelming advantages. To provide ubiquitous always-on access, a cloud service provider (CSP) maintains multiple replicas for each piece of data on geographically distributed servers. A key problem of using the replication technique in clouds is that it is very expensive to achieve strong consistency on a worldwide scale. In this paper, we first present a novel consistency as a service (CaaS) model, which consists of a large data cloud and multiple small audit clouds. In the CaaS model, a data cloud is maintained by a CSP, and a group of users that constitute an audit cloud can verify whether the data cloud provides the promised level of consistency or not. We propose a two-level auditing architecture, which only requires a loosely synchronized clock in the audit cloud. Then, we design  Algorithms to quantify the severity of violations with two metrics: the commonality of violations, and the staleness of the value of a read. Finally, we devise a heuristic auditing strategy (HAS) to reveal as many violations as possible. Extensive experiments were performed using a combination of simulations and real cloud deployments to validate HAVE.

Data Similarity-Aware Computation Infrastructure for the Cloud

Abstract—the cloud is emerging for scalable and efficient cloud services. To meet the needs of handling massive data and decreasing data migration, the computation infrastructure requires efficient data placement and proper management for cached data. In this paper, we propose an efficient and cost-effective multilevel caching scheme, called MERCURY, as computation infrastructure of the cloud. The idea behind MERCURY is to explore and exploit data similarity and support efficient data placement. To accurately and efficiently capture the data similarity, we leverage a low-complexity locality-sensitive hashing (LSH). In our design, in addition to the problem of space inefficiency, we identify that a conventional LSH scheme also suffers from the problem of homogeneous data placement. To address these two problems, we design a novel multi core-enabled locality-sensitive hashing (MC-LSH) that accurately captures the differentiated similarity across data. The similarity-aware MERCURY, hence, partitions data into the L1 cache, L2 cache, and main memory based on their distinct localities, which help optimize cache utilization and minimize the pollution in the last-level cache. Besides extensive evaluation through simulations, we also implemented MERCURY in a system. Experimental results based

On real-world applications and data sets demonstrate the efficiency and efficacy of our proposed schemes.

Maximizing Revenue with Dynamic Cloud Pricing: The Infinite Horizon Case

Abstract—we study the infinite horizon dynamic pricing problem for an infrastructure cloud provider in the emerging cloud computing paradigm. The cloud provider, such as Amazon, provides computing capacity in the form of virtual instances and charges customers a time-varying price for the period they

use the instances. The provider’s problem is then to find an optimal pricing policy, in face of stochastic demand arrivals and departures, so that the average expected revenue is maximized in the long run. We adopt a revenue management framework to tackle the problem. Optimality conditions and structural results are obtained for our stochastic formulation, which yield insights on the optimal pricing strategy. Numerical results verify our analysis and reveal additional properties of optimal pricing policies for the

Infinite horizon case.

Enabling Data Integrity Protection in Regenerating-Coding-Based Cloud Storage

Abstract—to protect outsourced data in cloud storage against corruptions, enabling integrity protection, fault tolerance, and efficient recovery for cloud storage becomes critical. Regenerating codes provide fault tolerance by striping data across multiple servers, while using less repair traffic than traditional erasure codes during failure recovery. Therefore, we study the problem of remotely checking the integrity of regenerating-coded data against corruptions under a real-life cloud storage setting. We

Design and implement a practical data integrity protection (DIP) scheme for a specific regenerating code, while preserving the intrinsic properties of fault tolerance and repair traffic saving. Our DIP scheme is designed under a Byzantine adversarial model, and enables a client to feasibly verify the integrity of random subsets of outsourced data against general or malicious corruptions. It works under the simple assumption of thin-cloud storage and allows different parameters to be fine-tuned for the performance-security trade-off. We implement and evaluate the overhead of our DIP scheme in a real cloud storage test bed under different parameter choices. We demonstrate that remote integrity checking can be feasibly integrated into regenerating codes in practical deployment.

Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage

Abstract—Data sharing is an important functionality in cloud storage. In this article, we show how to securely, efficiently, and flexibly share data with others in cloud storage. We describe new public-key cryptosystems which produce constant-size cipher texts such that efficient delegation of decryption rights for any set of cipher texts are possible. The novelty is that one can aggregate any set of secret keys and make them as compact as a single key, but encompassing the power of all the keys being aggregated. In other words, the secret key holder can release a constant-size aggregate key for flexible choices of cipher text set in cloud storage, but the other encrypted files outside the set remain confidential. This compact aggregate key can be conveniently sent to others or be stored in a smart card with very limited secure storage. We provide formal security analysis of our schemes in the standard model. We also describe other application of our schemes. In particular, our schemes give the first public-key patient-controlled encryption for flexible hierarchy, which was yet to be known.

Low-Carbon Routing Algorithms for Cloud Computing Services in IP-over-WDM Networks

                                                                                                               Abstract—Energy consumption in telecommunication networks keeps growing rapidly, mainly due to emergence of new Cloud Computing (CC) services that need to be supported by large data centers that consume a huge amount of energy and, in turn, cause the emission of enormous quantity of CO2. Given the decreasing availability of fossil fuels and the raising concern about global warming, research is now focusing on novel “low-carbon” telecom solutions. E.g., based on today telecom technologies, data centers can be located near renewable energy plants and data can then be effectively transferred to these locations via reconfigurable optical networks, based on the principle that data can be moved more efficiently than electricity. This paper focuses on how to dynamically route on-demand optical circuits that are established to transfer energy-intensive data processing towards data centers powered with renewable energy. Our main contribution consists in devising two routing algorithms for connections supporting CC services, aimed at minimizing the CO2 emissions of data centers by following the current availability of renewable energy (Sun and Wind). The trade-off with energy consumption for the transport equipments is also considered. The results show that relevant reductions, up to about 30% in CO2 emissions can be achieved using our approaches compared to baseline shortest path- based routing strategies, paying off only a marginal increase in terms of network blocking probability

NCCloud: A Network-Coding-Based Storage System in a Cloud-of-Clouds

Abstract—to provide fault tolerance for cloud storage, recent studies propose to stripe data across multiple cloud vendors. However, if a cloud suffers from a permanent failure and loses all its data, we need to repair the lost data with the help of the other surviving clouds to preserve data redundancy. We present a proxy-based storage system for fault-tolerant multiple-cloud storage called NCCloud, which achieves cost-effective repair for a permanent single-cloud failure. NCCloud is built on top of a network-coding-based storage scheme called the functional minimum-storage regenerating (FMSR) codes, which maintain the same fault tolerance and data redundancy as in traditional erasure codes (e.g., RAID-6), but use less repair traffic and hence incur less monetary cost due to data transfer. One key design feature of our FMSR codes is that we relax the encoding requirement of storage nodes during repair, while preserving the benefits of network coding in repair. We implement a proof-of-concept prototype of NCCloud and deploy it atop both local and commercial clouds. We validate that FMSR codes provide significant monetary cost savings in repair over RAID-6 codes, while having comparable response time performance in normal cloud storage operations such as upload/download.

Integrity Verification in Multi-Cloud Storage Using Cooperative Provable Data Possession

Abstract- Storage outsourcing in cloud computing is a rising trend which prompts a number of interesting security issues. Provable data possession (PDP) is a method for ensuring the integrity of data in storage outsourcing. This research addresses the construction of efficient PDP which called as Cooperative PDP (CPDP) mechanism for distributed cloud storage to support data migration and scalability of service, which considers the existence of multiple cloud service providers to collaboratively store and maintain the clients’ data. Cooperative PDP (CPDP) mechanism is based on homomorphic verifiable response, hash index hierarchy for dynamic scalability, cryptographic encryption for security. Moreover, it proves the security of scheme based on multi-prover zero knowledge proof system, which can satisfy knowledge soundness, completeness, and zero-knowledge properties. This research introduces lower computation and communication overheads in comparison with non-cooperative approaches.

Optimal Power Allocation and Load Distribution for Multiple Heterogeneous Multi core Server Processors across Clouds and Data Centers

Abstract—for multiple heterogeneous multi core server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large scale server systems in current and future data centers. The multi core processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multi core server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multi core server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud

Abstract—with cloud storage services, it is commonplace for data to be not only stored in the cloud, but also shared across multiple users. However, public auditing for such shared data — while preserving identity privacy — remains to be an open challenge. In this paper, we propose the first privacy-preserving mechanism that allows public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute the verification information needed to audit the integrity of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from a third party auditor (TPA), who is still able to verify the integrity of shared data without retrieving the entire file. Our experimental results demonstrate the effectiveness and efficiency of our proposed mechanism when auditing shared data.

Towards Differential Query Services in Cost-Efficient Clouds

Abstract—Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In a cost efficient cloud environment, a user can tolerate a certain degree of delay while retrieving information from the cloud to reduce costs. In this paper, we address two fundamental issues in such an environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme that was originally proposed by Ostrovsky. Their scheme allows a user to retrieve files of interest from an un trusted server without leaking any information. The main drawback is that it will cause a heavy querying overhead incurred on the cloud, and thus goes against the original intention of cost efficiency. In this paper, we present a scheme, termed efficient information retrieval for ranked query (EIRQ), based on an aggregation and distribution layer (ADL), to reduce querying overhead incurred on the cloud. In EIRQ, queries are classified into multiple ranks, where a higher ranked query can retrieve a higher percentage of matched files. A user can retrieve files on demand by choosing queries of different ranks. This feature is useful when there are a large number of matched files, but the user only needs a small subset of them. Under different parameter settings, extensive evaluations have been conducted on both analytical models and on a real cloud environment, in order to examine the effectiveness of our schemes.

Scalable Distributed Service Integrity Attestation for Software-as-a-Service Clouds

Abstract—Software-as-a-Service (SaaS) cloud systems enable application service providers to deliver their applications via massive cloud computing infrastructures. However, due to their sharing nature, SaaS clouds are vulnerable to malicious attacks. In this paper, we present IntTest, a scalable and effective service integrity attestation framework for SaaS clouds. Int Test provides a novel integrated attestation graph analysis scheme that can provide stronger attacker pinpointing power than previous schemes. Moreover, IntTest can automatically enhance result quality by replacing bad results produced by malicious attackers with good results produced by benign service providers. We have implemented a prototype of the IntTest system and tested it on a production cloud computing infrastructure using IBM System S stream processing applications. Our experimental results show that IntTest can achieve higher attacker pinpointing accuracy than existing approaches. IntTest does not require any special hardware or secure kernel support and imposes little performance impact to the application, which makes it practical for large scale cloud systems.

QoS-Aware Data Replication for Data-Intensive Applications in Cloud Computing Systems

Abstract—Cloud computing provides scalable computing and storage resources. More and more data-intensive applications are developed in this computing environment. Different applications have different quality-of-service (QoS) requirements. To continuously support the QoS requirement of an application after data corruption, we propose two QoS-aware data replication (QADR) algorithms in cloud computing systems. The first algorithm adopts the intuitive idea of high-QoS first-replication (HQFR) to perform data replication. However, this greedy algorithm cannot minimize the data replication cost and the number of QoS-violated data replicas. To achieve these two minimum objectives, the second algorithm transforms the QADR problem into the well-known minimum-cost maximum-flow (MCMF) problem. By applying the existing MCMF algorithm to solve the QADR problem, the second algorithm can produce the optimal solution to the QADR problem in polynomial time, but it takes more computational time than the first algorithm. Moreover, it is known that a cloud computing system usually has a large number of nodes. We also propose node combination techniques to reduce the possibly large data replication time. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed algorithms in the data replication and recovery.

Public Auditing for Shared Data with Efficient User Revocation in the Cloud

Abstract—with data services in the cloud, users can easily modify and share data as a group. To ensure data integrity can be audited publicly, users need to compute signatures on all the blocks in shared data. Different blocks are signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks, which were previously signed by this revoked user, must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is
Inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.


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