PriSense: Privacy-Preserving Data Aggregation in People-Centric Urban Sensing Systems

Author

Shi, Jing ; Zhang, Rui ; Liu, Yunzhong ; Zhang, Yanchao

Author_Institution

Dept. of Electr. & Comput. Eng., New Jersey Inst. of Technol., Newark, NJ, USA

fYear

2010

fDate

14-19 March 2010

Firstpage

1

Lastpage

9

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 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.

Keywords

data handling; data privacy; mobile computing; statistics; PriSense; data aggregation; data mixing; data slicing; non-additive aggregation function; people-centric urban sensing; privacy preservation; statistical additive function; Aggregates; Communications Society; Computer architecture; Data privacy; Distributed computing; Histograms; Personal digital assistants; Sensor systems; Space technology; Statistical distributions;

fLanguage

English

Publisher

ieee

Conference_Titel

INFOCOM, 2010 Proceedings IEEE

Conference_Location

San Diego, CA

ISSN

0743-166X

Print_ISBN

978-1-4244-5836-3

Type

conf

DOI

10.1109/INFCOM.2010.5462147

Filename

5462147