Scalable privacy-preserving big data aggregation mechanism

13Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.

Abstract

As the massive sensor data generated by large-scale Wireless Sensor Networks (WSNs) recently become an indispensable part of ‘Big Data’, the collection, storage, transmission and analysis of the big sensor data attract considerable attention from researchers. Targeting the privacy requirements of large-scale WSNs and focusing on the energy-efficient collection of big sensor data, a Scalable Privacy-preserving Big Data Aggregation (Sca-PBDA) method is proposed in this paper. Firstly, according to the pre-established gradient topology structure, sensor nodes in the network are divided into clusters. Secondly, sensor data is modified by each node according to the privacy-preserving configuration message received from the sink. Subsequently, intra- and inter-cluster data aggregation is employed during the big sensor data reporting phase to reduce energy consumption. Lastly, aggregated results are recovered by the sink to complete the privacy-preserving big data aggregation. Simulation results validate the efficacy and scalability of Sca-PBDA and show that the big sensor data generated by large-scale WSNs is efficiently aggregated to reduce network resource consumption and the sensor data privacy is effectively protected to meet the ever-growing application requirements.

Cite

CITATION STYLE

APA

Wu, D., Yang, B., & Wang, R. (2016). Scalable privacy-preserving big data aggregation mechanism. Digital Communications and Networks, 2(3), 122–129. https://doi.org/10.1016/j.dcan.2016.07.001

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free