Clustering-Anonymization-Based Differential Location Privacy Preserving Protocol in WSN

Abstract

Playing a vital role in the period of big data and intelligent life, wireless sensor networks (WSN) transmits a bulk of data. Location information as the vital data in transmission is widely used in detecting and routing for the network. With the big data mining and analysis, the security of location and data privacy in WSN faces great challenges. To the problem of active attacking like node capture in wireless sensor network node location privacy, existing location privacy preserving protocols are analyzed and Differential Location Privacy protocol based on Clustering Anonymization is proposed. By sensor nodes clustering using genetic clustering algorithm, the individual location is hidden in the statistical location information of the group. The Laplace Mechanism is also added to the protocol to realize differential location privacy. Node location privacy in WSN is preserved as well as privacy preserving budget is saved. The result of theoretical analysis and contrastive simulation experience shows that the protocol can be useful.