PDA: A Novel Privacy-Preserving Robust Data Aggregation Scheme in People-Centric Sensing System

Abstract

With the advancement of wireless communication technologies, mobile phones, PDAs, and car embedded devices are equipped with sensors, such as sound and image. People can apply these devices to form a new sensing network called people-centric sensing network. And this network offers new opportunities for cooperative sensing applications. However, it introduces some challenges, including security challenge and robust challenge. As sensor nodes need to send their individual sensed data to an aggregator node and these data are related to users' real life, privacy-preserving data aggregation is a challenge issue. As a node could become offline or a message could be lost before reaching the aggregator, retaining the correctness of the aggregate computed is important. In this paper, we present the design of PDA, a novel privacy-preserving robust data aggregation scheme in people-centric sensing system. Based on K-anonymity, homomorphic encryption, and secret sharing, PDA can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Subtraction, Average, Count, Max/Min, and Median without leaking individual sensed data. Moreover, PDA is robust to node failure and data loss. We also evaluate the efficacy and efficiency of PDA. The result shows that our scheme can achieve the security and robust goal under a reasonable cost.