End-to-end security framework for big sensing data streams

Deepak Puthal, Surya Nepal, Rajiv Ranjan, Jinjun Chen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Big data streaming has become an important paradigm for real-time processing of massive continuous data flows in large-scale sensing networks. While dealing with big sensing data streams from Internet of Things (IoT), a data stream manager (DSM) must always verify the authenticity, integrity, and confidentiality of the data to ensure end-to-end security as the medium of communication is wireless and untrusted. Malicious attackers could access and modify the data at any time/place from source to cloud data center. Existing technologies for data security verification are not suitable for data-streaming applications, as the verification should be performed in real time and which introduces a delay in the data stream. In this chapter, we will propose a Dynamic Prime-Number- Based Security Verification (DPBSV) framework for big data streams. Our framework is based on a common shared key that is updated dynamically by generating synchronized prime numbers. The common shared key updates at both ends, that is, source-sensing devices and DSM, without further communication after handshaking. Theoretical analyses and experimental results of our DPBSV framework show that it can significantly improve the efficiency of the verification process by reducing the time and utilizing a smaller buffer size in DSM.We have experimented the proposed scheme in a simulated environment and demonstrated the feasibility of the approach. We observed that the proposed scheme not only reduces the verification time or buffer size in DSM, but also strengthens the security of the data by constantly changing the shared keys.

Original languageBritish English
Title of host publicationBig Data Management and Processing
Number of pages16
ISBN (Electronic)9781498768085
StatePublished - 1 Jan 2017


Dive into the research topics of 'End-to-end security framework for big sensing data streams'. Together they form a unique fingerprint.

Cite this