A Balance Privacy-Preserving Data Aggregation Model in Wireless Sensor Networks

Abstract

Wireless sensor networks are always deployed in remote and hostile environments to gather sensitive information, in which sensor nodes are apt to encounter some serious leakage of sensitive data. Hence, privacy-preserving is becoming an increasingly important issue in security data aggregation for wireless sensor networks. In this paper, we propose a balance privacy-preserving data aggregation model (BPDA) based on slicing and mixing technology. Compared to fixed or random slicing, BPDA model gives a balance slicing mechanism to ensure that slice can be sent to the nodes which have lower privacy preservation and enhance the privacy-preserving efficacy. Furthermore, according to the influence of the node degree and energy, three different schemes are presented to keep the privacy-preserving data aggregation balance. Theoretical analysis and simulation show that BPDA model demonstrates a good performance in terms of privacy-preserving efficacy and communication overhead and prolongs the lifetime of network.