Prisense: privacy-preserving data aggregation in people-centric urban sensing systems

Authors:
Jing Shi;Rui Zhang;Yunzhong Liu;Yanchao Zhang
Affiliations:
Department of Electrical and Computer Engineering, New Jersey Institute of Technology;Department of Electrical and Computer Engineering, New Jersey Institute of Technology;Department of Electrical and Computer Engineering, New Jersey Institute of Technology;Department of Electrical and Computer Engineering, New Jersey Institute of Technology
Venue:
INFOCOM'10 Proceedings of the 29th conference on Information communications
Year:
2010

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Abstract

People-centric urban sensing is a new paradigm gaining popularity. A main obstacle to its widespread deployment and adoption are the privacy concerns of participating individuals. To tackle this open challenge, this paper presents the design and evaluation of PriSense, a novel solution to privacypreserving data aggregation in people-centric urban sensing systems. PriSense is based on the concept of data slicing and mixing and 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. PriSense can support strong user privacy against a tunable threshold number of colluding users and aggregation servers. The efficacy and efficiency of PriSense are confirmed by thorough analytical and simulation results.