DPBSV- An Efficient and Secure Scheme for Big Sensing Data Stream

Abstract

Stream processing has become an important paradigm for the massive real-time processing of continuous data flows in large scale sensor networks. While dealing with big data streams in sensor networks, Stream Processing Engines (SPEs) must always verify the authenticity, and integrity of the data as the medium of communication is untrusted, as malicious attackers could access and modify the data. Existing technologies for data security verification are not suitable for data streaming applications, as the verification in real time introduces significant overheads. In this paper, we propose a Dynamic Prime Number Based Security Verification (DPBSV) scheme for big data stream processing. Our scheme is based on a common shared key that is updated dynamically by generating synchronized pairs of prime numbers. Theoretical analyses and experimental results of our DPBSV scheme show that it can significantly improve the efficiency as compared to existing approaches by reducing the security verification overhead. Our approach not only reduces the verification time, but also strengthens the security of the data by constantly updating the shared keys.