Verifiable Privacy-Preserving Aggregation in People-Centric Urban Sensing Systems

Abstract

People-centric urban sensing systems (PC-USSs) refer to using human-carried mobile devices such as smartphones and tablets for urban-scale distributed data collection, analysis, and sharing to facilitate interaction between humans and their surrounding environments. A main obstacle to the widespread deployment and adoption of PC-USSs are the privacy concerns of participating individuals as well as the concerns about data integrity. To tackle this open challenge, this paper presents the design and evaluation of VPA, a novel peer-to-peer based solution to verifiable privacy-preserving data aggregation in PC-USSs. VPA achieves strong user privacy by letting each user exchange random shares of its datum with other peers, while at the same time ensures data integrity through a combination of Trusted Platform Module and homomorphic message authentication code. VPA can support a wide range of statistical additive and non-additive aggregation functions such as Sum, Average, Variance, Count, Max/Min, Median, Histogram, and Percentile with accurate aggregation results. The efficacy and efficiency of VPA are confirmed by thorough analytical and simulation results.