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 language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE Conference on Communications and Network Security, CNS 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 145-153 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781509030651 |
| DOIs | |
| State | Published - Feb 21 2017 |
| Event | 2016 IEEE Conference on Communications and Network Security, CNS 2016 - Philadelphia, United States Duration: Oct 17 2016 → Oct 19 2016 |
Other
| Other | 2016 IEEE Conference on Communications and Network Security, CNS 2016 |
|---|---|
| Country | United States |
| City | Philadelphia |
| Period | 10/17/16 → 10/19/16 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Safety, Risk, Reliability and Quality
Cite this
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 proceeding › Conference contribution
}
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.
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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
AN - SCOPUS:85015988211
SP - 145
EP - 153
BT - 2016 IEEE Conference on Communications and Network Security, CNS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -