A generic approach for integrity verification of big data

Abstract

Data integrity verification is a primary issue for the prominent and unchallenged success of Big Data. The large scale of data includes extra security overheads on customers that used Big Data services. In Big Data environment, many security challenges have pinched out such as data security, malicious insider attack, cyber attack, and abuse of data. In this paper, we have analyzed and identified a novel data integrity verification technique that provides better performance, efficiency, and quick review of Big Data. The milestone of our work are algebraic signature, homomorphic tag, and Combinatorial Batch Codes (CBC). Homomorphic tag delegates a special verifiable value to each data blocks. CBC allocates integral data to store on Big Data servers. This research work used multiple third-party auditors for batch auditing which ensure data integrity in a Big Data environment. Our approach handles single point failure error of single Third Party Auditor (TPA) efficiently. Without using any additional data structure, it supports dynamic data operations. The security and performance analysis with comparative results show the genuine application of our approach within the latest Big Data environment.