Towards secure big data analytic for cloud-enabled applications with fully homomorphic encryption.

Cloud computing empowers enterprises to efficiently manage big data and discovery of useful information which are the most fundamental challenges for big data enabled applications. The cloud offered unlimited resources that can store, manage and analyze massive and heterogeneous data to improve the quality assurance of application services. Nevertheless, cloud computing is exposed to enormous external and internal privacy breaches and leakage threats. In this paper, we introduce a privacy-preserving distributed analytics framework for big data in cloud. Fully Homomorphic Encryption (FHE) is used as an emerging and powerful cryptosystem that can carry out analysis tasks on encrypted data. The developed distributed approach has the scalability to partition both data and analysis computations into subset cloud computing nodes that can be run independently. This rapidly accelerates the performance of encrypted data processing while preserving a high level of analysis accuracy. Our experimental evaluation demonstrates the efficiency of the proposed framework, in terms of both analysis performance and accuracy, for building a secure analytics cloud-enabled application. • A privacy-preserving distributed analytics framework for big data in the cloud is proposed. • Fully Homomorphic Encryption (FHE) is adapted to protect the privacy of outsourced data while it is stored and processed in cloud computing. • Distributed analytics approach is proposed which can be applied to several distributed-based applications. • The distributed approach improves the scalability of analytic tasks in a way that ensures a high level of analysis accuracy while reducing the performance overhead of encrypted computations. [ABSTRACT FROM AUTHOR]