Security-Aware Efficient Mass Distributed Storage Approach for Cloud Systems in Big Data

Abstract

Implementing cloud computing empowers numerous paths to Web-based computing service offerings for meeting diverse needs. However, cloud data security and privacy information protection have also become a critical issue restraining the cloud applications. One of the major concerns in security is that cloud operators will have a chance to reach sensitive data, which dramatically increases users' anxiety and reduces the adoptability of cloud computing in many fields, such as the financial industry and governmental agencies. This paper focuses on this issues and proposes a novel approach that can efficiently split the file and separately store the data in the distributed cloud servers, in which the data cannot be directly reached by cloud service operators. The proposed scheme is entitled as Security-Aware Efficient Distributed Storage (SAEDS) model, which is mainly supported by the proposed algorithms, named Secure Efficient Data Distributions (SED2) Algorithm and Efficient Data Conflation (EDCon) Algorithm. Our experimental evaluations have assessed both security and efficiency performances.