BidGuard

A framework for privacy-preserving crowdsensing incentive mechanisms

Jian Lin, Dejun Yang, Ming Li, Jia Xu, Guoliang Xue

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

9 Citations (Scopus)

Abstract

With the rapid growth of smartphones, crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Auction has been widely used to design mechanisms to stimulate smartphone users to participate in the crowdsensing applications and systems. Many auction-based incentive mechanisms have been proposed for crowdsensing. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. To the best of our knowledge, we are the first to study the design of privacy-preserving incentive mechanisms that also achieve approximate social cost minimization. In this paper, we design BidGuard, a general privacy-preserving framework for incentivizing crowdsensing. This framework works with different score functions for selecting users. In particular, we propose two score functions, linear and log functions, to realize the framework. We rigorously prove that BidGuard achieves computational efficiency, individual rationality, truthfulness, differential privacy and approximate social cost minimization. In addition, the BidGuard with log score function is asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance and validate the desired properties of BidGuard.

Original languageEnglish (US)
Title of host publication2016 IEEE Conference on Communications and Network Security, CNS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages145-153
Number of pages9
ISBN (Electronic)9781509030651
DOIs
StatePublished - Feb 21 2017
Event2016 IEEE Conference on Communications and Network Security, CNS 2016 - Philadelphia, United States
Duration: Oct 17 2016Oct 19 2016

Other

Other2016 IEEE Conference on Communications and Network Security, CNS 2016
CountryUnited States
CityPhiladelphia
Period10/17/1610/19/16

Fingerprint

Smartphones
Costs
Computational efficiency
Sensors

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

Cite this

Lin, J., Yang, D., Li, M., Xu, J., & Xue, G. (2017). BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms. In 2016 IEEE Conference on Communications and Network Security, CNS 2016 (pp. 145-153). [7860480] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CNS.2016.7860480

BidGuard : A framework for privacy-preserving crowdsensing incentive mechanisms. / Lin, Jian; Yang, Dejun; Li, Ming; Xu, Jia; Xue, Guoliang.

2016 IEEE Conference on Communications and Network Security, CNS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 145-153 7860480.

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

Lin, J, Yang, D, Li, M, Xu, J & Xue, G 2017, BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms. in 2016 IEEE Conference on Communications and Network Security, CNS 2016., 7860480, Institute of Electrical and Electronics Engineers Inc., pp. 145-153, 2016 IEEE Conference on Communications and Network Security, CNS 2016, Philadelphia, United States, 10/17/16. https://doi.org/10.1109/CNS.2016.7860480
Lin J, Yang D, Li M, Xu J, Xue G. BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms. In 2016 IEEE Conference on Communications and Network Security, CNS 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 145-153. 7860480 https://doi.org/10.1109/CNS.2016.7860480
Lin, Jian ; Yang, Dejun ; Li, Ming ; Xu, Jia ; Xue, Guoliang. / BidGuard : A framework for privacy-preserving crowdsensing incentive mechanisms. 2016 IEEE Conference on Communications and Network Security, CNS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 145-153
@inproceedings{205647dd5baa475ba998e90540d9dfe6,
title = "BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms",
abstract = "With the rapid growth of smartphones, crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Auction has been widely used to design mechanisms to stimulate smartphone users to participate in the crowdsensing applications and systems. Many auction-based incentive mechanisms have been proposed for crowdsensing. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. To the best of our knowledge, we are the first to study the design of privacy-preserving incentive mechanisms that also achieve approximate social cost minimization. In this paper, we design BidGuard, a general privacy-preserving framework for incentivizing crowdsensing. This framework works with different score functions for selecting users. In particular, we propose two score functions, linear and log functions, to realize the framework. We rigorously prove that BidGuard achieves computational efficiency, individual rationality, truthfulness, differential privacy and approximate social cost minimization. In addition, the BidGuard with log score function is asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance and validate the desired properties of BidGuard.",
author = "Jian Lin and Dejun Yang and Ming Li and Jia Xu and Guoliang Xue",
year = "2017",
month = "2",
day = "21",
doi = "10.1109/CNS.2016.7860480",
language = "English (US)",
pages = "145--153",
booktitle = "2016 IEEE Conference on Communications and Network Security, CNS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - BidGuard

T2 - A framework for privacy-preserving crowdsensing incentive mechanisms

AU - Lin, Jian

AU - Yang, Dejun

AU - Li, Ming

AU - Xu, Jia

AU - Xue, Guoliang

PY - 2017/2/21

Y1 - 2017/2/21

N2 - With the rapid growth of smartphones, crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Auction has been widely used to design mechanisms to stimulate smartphone users to participate in the crowdsensing applications and systems. Many auction-based incentive mechanisms have been proposed for crowdsensing. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. To the best of our knowledge, we are the first to study the design of privacy-preserving incentive mechanisms that also achieve approximate social cost minimization. In this paper, we design BidGuard, a general privacy-preserving framework for incentivizing crowdsensing. This framework works with different score functions for selecting users. In particular, we propose two score functions, linear and log functions, to realize the framework. We rigorously prove that BidGuard achieves computational efficiency, individual rationality, truthfulness, differential privacy and approximate social cost minimization. In addition, the BidGuard with log score function is asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance and validate the desired properties of BidGuard.

AB - With the rapid growth of smartphones, crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Auction has been widely used to design mechanisms to stimulate smartphone users to participate in the crowdsensing applications and systems. Many auction-based incentive mechanisms have been proposed for crowdsensing. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. To the best of our knowledge, we are the first to study the design of privacy-preserving incentive mechanisms that also achieve approximate social cost minimization. In this paper, we design BidGuard, a general privacy-preserving framework for incentivizing crowdsensing. This framework works with different score functions for selecting users. In particular, we propose two score functions, linear and log functions, to realize the framework. We rigorously prove that BidGuard achieves computational efficiency, individual rationality, truthfulness, differential privacy and approximate social cost minimization. In addition, the BidGuard with log score function is asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance and validate the desired properties of BidGuard.

UR - http://www.scopus.com/inward/record.url?scp=85015988211&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015988211&partnerID=8YFLogxK

U2 - 10.1109/CNS.2016.7860480

DO - 10.1109/CNS.2016.7860480

M3 - Conference contribution

SP - 145

EP - 153

BT - 2016 IEEE Conference on Communications and Network Security, CNS 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -