PriSense: Privacy-preserving data aggregation in people-centric urban sensing systems

Jing Shi, Rui Zhang, Yunzhong Liu, Yanchao Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

204 Scopus citations


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 privacy-preserving 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.

Original languageEnglish (US)
Title of host publication2010 Proceedings IEEE INFOCOM
StatePublished - Jun 15 2010
Externally publishedYes
EventIEEE INFOCOM 2010 - San Diego, CA, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Country/TerritoryUnited States
CitySan Diego, CA

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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