IEEE Transactions on Big Data

From the April-June 2016 issue

Deduplication on Encrypted Big Data in Cloud

By Zheng Yan, Wenxiu Ding, Xixun Yu, Haiqi Zhu, and Robert H. Deng

Featured article thumbnail imageCloud computing offers a new way of service provision by re-arranging various resources over the Internet. The most important and popular cloud service is data storage. In order to preserve the privacy of data holders, data are often stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for big data storage and processing in cloud. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. They cannot flexibly support data access control and revocation. Therefore, few of them can be readily deployed in practice. In this paper, we propose a scheme to deduplicate encrypted data stored in cloud based on ownership challenge and proxy re-encryption. It integrates cloud data deduplication with access control. We evaluate its performance based on extensive analysis and computer simulations. The results show the superior efficiency and effectiveness of the scheme for potential practical deployment, especially for big data deduplication in cloud storage.

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Editorials and Announcements


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Guest Editorials

Call for Papers

Special Issue on Big Data for Cyber-Physical Systems

Extended Submission deadline: August 12, 2016. View PDF.

Cyber-Physical Systems (CPS) are characterized by the deep complex intertwining among cyber components and physical components. Due to the fast increase in system complexities, the operations of CPS involve sensing, processing and storage of massive amount of data. This nature of “big data” imposes fundamental challenges on the design and management of CPS in multiple aspects such as performance, energy efficiency, security, privacy, reliability, sustainability, fault tolerance, scalability and flexibility. Tackling these challenges necessitates innovative big data techniques for handling massive data in CPS. This special issue will present the state-of-the-art research results on the topic of big data sensing, processing and storage for CPS, and stimulate a broad range of researchers to participate in the interdisciplinary CPS research in the future.

Special Issue on Big Data Applications in Cyber Security and Threat Intelligence

Submission deadline: September 1, 2016. View PDF.

This last decade has witnessed a tremendous rapid increase in volume, veracity, velocity and variety of data generated by different cyber security solutions and as part of cyber investigation cases. When a significant amount of data is collected from or generated by different devices and sources, intelligent big-data analytical techniques are necessary to mine, interpret and visualise such data. To mitigate existing cyber security threats, it is important for big-data analytical techniques to keep pace.

This special issue will focus on cutting-edge from both academia and industry, with a particular emphasis on novel techniques to mine, interpret and visualise big-data from a wide range of sources and can be applied in cyber security, cyber forensics and threat intelligence context. Only technical papers describing previously unpublished, original, state-of-the-art research, and not currently under review by a conference or a journal will be considered. Extended work must have a significant number of "new and original" ideas/contributions along with more than 30% brand "new" material.

Special Issue on Big Data Systems on Emerging Architectures

Submission deadline: October 15, 2016. View PDF.

The continued evolution of computing hardware and infrastructure imposes new challenges and bottlenecks to big data management. Over the last few years there has been a renewed interest in the area of (big) data systems on emerging hardware. The opportunities and challenges from emerging computing systems have been raised different scales, from a single machine to thousands of machines. The need for effectively utilizing computing resources creates new technologies and research directions: from conventional ones (e.g., cluster computing, in-memory computing), to more recent ones (e.g., GPGPU, many-core processors, and NVRAM). In addition to performance, many other system features are important for big data applications, like energy consumption and total ownership costs. For a specific application domain such as graph processing and deep learning, the design and development of novel systems on emerging hardware will create the insight into new solution approaches of the application domain and even further. Thus, there is a need to fundamentally address all the above-mentioned issues in big data systems. IEEE Transaction on Big Data (TBD) seeks original manuscripts for a Special Issue on the theme - Big Data Systems on Emerging Architectures scheduled to appear in an issue of 2017.

Special Issue on Trustworthiness in Big Data and Cloud Computing Systems

Submission deadline: January 15, 2017. View PDF.

The rapid advancement of digital sensors, computers, networks, and smart devices with their extensive use is leading to the integration of a significant amount of diversified data that results in emerging research on Big Data. Cloud computing means storing, computing, and accessing data and programs over the Internet. The growth of cloud computing and cloud data stores have been a precursor and facilitator to the emergence of Big Data. Thus, Big Data and Cloud systems are considered complimentary to each other.

Since Big Data are often in unstructured or semi-structured forms that are being generated from various sources, trustworthiness in data collection, integration, computing, decision-making, and data management becomes a great concern. For example, can we trust current Big Data storage and protection systems or can the use of Big Data analytic enhance security and privacy of the whole system? On the other hand, trustworthiness is also one of the most concerning issues in Cloud Computing environments in terms of fault tolerance, data loss recovery, data privacy/security/safety, and data protection, due to its open environment with very limited end user-side controls. Currently, many new applications are being developed explicitly for cloud system deployment, while many traditional applications will eventually evolve to cloud. The end user-side wants these cloud-based services to be at least as trustworthy and available as traditional offerings. To meet these expectations, cloud service providers and cloud consumers need to gain a solid understanding of the unique challenges of cloud computing and learn how to mitigate risks.

While information society, commercial and scientific companies, and industries share the need for massive throughput, trustworthiness of services will become a big concern. However, trustworthiness in both Big Data and Cloud Computing systems has received less attention from researchers and practitioners. The aim of this special issue is to solicit both original research that discusses the trustworthiness issues, trustworthy platforms, trustworthy frameworks, and design methodologies for Big Data and Cloud Computing systems.

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TBD is financially cosponsored by:

IEEE Computer SocietyIEEE Communications SocietyIEEE Computational Intelligence SocietyIEEE Sensors CouncilIEEE Consumer Electronics Society


IEEE Signal Processing SocietyIEEE Systems, Man, & Cybernetics SocietyIEEE Systems CouncilIEEE Vehicular Technology Society


TBD is technically cosponsored by:

IEEE Control Systems SocietyIEEE Photonics SocietyIEEE Engineering in Medicine & Biology SocietyIEEE Power & Energy SocietyIEEE Biometrics Council