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

Jing Shi; Rui Zhang; Yunzhong Liu; Yanchao Zhang

doi:10.1109/INFCOM.2010.5462147

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 proceeding › Conference contribution

205 Scopus citations

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.

Original language	English (US)
Title of host publication	2010 Proceedings IEEE INFOCOM
DOIs	https://doi.org/10.1109/INFCOM.2010.5462147
State	Published - 2010
Externally published	Yes
Event	IEEE INFOCOM 2010 - San Diego, CA, United States Duration: Mar 14 2010 → Mar 19 2010

Publication series

Name	Proceedings - IEEE INFOCOM
ISSN (Print)	0743-166X

Other

Other	IEEE INFOCOM 2010
Country/Territory	United States
City	San Diego, CA
Period	3/14/10 → 3/19/10

ASJC Scopus subject areas

General Computer Science
Electrical and Electronic Engineering

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10.1109/INFCOM.2010.5462147

Cite this

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title = "PriSense: Privacy-preserving data aggregation in people-centric urban sensing systems",

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.",

author = "Jing Shi and Rui Zhang and Yunzhong Liu and Yanchao Zhang",

year = "2010",

doi = "10.1109/INFCOM.2010.5462147",

language = "English (US)",

isbn = "9781424458363",

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booktitle = "2010 Proceedings IEEE INFOCOM",

note = "IEEE INFOCOM 2010 ; Conference date: 14-03-2010 Through 19-03-2010",

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T2 - IEEE INFOCOM 2010

AU - Shi, Jing

AU - Zhang, Rui

AU - Liu, Yunzhong

AU - Zhang, Yanchao

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

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U2 - 10.1109/INFCOM.2010.5462147

DO - 10.1109/INFCOM.2010.5462147

M3 - Conference contribution

AN - SCOPUS:77953308558

SN - 9781424458363

T3 - Proceedings - IEEE INFOCOM

BT - 2010 Proceedings IEEE INFOCOM

Y2 - 14 March 2010 through 19 March 2010

ER -

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

Abstract

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