TY - JOUR
T1 - REAP
T2 - An efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing
AU - Zhang, Zhikun
AU - He, Shibo
AU - Chen, Jiming
AU - Zhang, Junshan
N1 - Funding Information:
Manuscript received October 18, 2017; revised February 7, 2018 and March 23, 2018; accepted April 24, 2018. Date of publication May 7, 2018; date of current version June 5, 2018. This work was supported by the Natural Science Foundation of China under Grant 61429301 and Grant U1401253. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Wei Yu. (Corresponding author: Jiming Chen.) Z. Zhang, S. He, and J. Chen are with the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China, and also with the Cyber Innovation Joint Research Center, Hangzhou 310027, China (e-mail: zhangzhk@zju.edu.cn; s18he@iipc.zju.edu.cn; cjm@zju.edu.cn).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2018/12
Y1 - 2018/12
N2 - Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies assume a trustworthy fusion center (FC) in their co-design of incentive mechanism and privacy preservation. Very recent work has taken the step to relax the assumption on trustworthy FC and allowed participatory users (PUs) to randomly report their binary sensing data, whereas the focus is to examine PUs' equilibrium behavior. Making a paradigm shift, this paper aims to study the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such a scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer injecting larger noises for higher privacy-preserving levels (PPLs). To strike a good balance therein, we propose an efficient incentive mechanism named REAP to reconcile FC's aggregation accuracy and individual PU's data privacy. Specifically, we adopt the celebrated notion of differential privacy to quantify PUs' PPLs and characterize their impacts on FC's aggregation accuracy. Then, appealing to contract theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control them. It can further overcome the information asymmetry problem, i.e., FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.
AB - Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies assume a trustworthy fusion center (FC) in their co-design of incentive mechanism and privacy preservation. Very recent work has taken the step to relax the assumption on trustworthy FC and allowed participatory users (PUs) to randomly report their binary sensing data, whereas the focus is to examine PUs' equilibrium behavior. Making a paradigm shift, this paper aims to study the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such a scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer injecting larger noises for higher privacy-preserving levels (PPLs). To strike a good balance therein, we propose an efficient incentive mechanism named REAP to reconcile FC's aggregation accuracy and individual PU's data privacy. Specifically, we adopt the celebrated notion of differential privacy to quantify PUs' PPLs and characterize their impacts on FC's aggregation accuracy. Then, appealing to contract theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control them. It can further overcome the information asymmetry problem, i.e., FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.
KW - Crowd sensing
KW - data aggregation
KW - incentive mechanism
KW - privacy preservation
UR - http://www.scopus.com/inward/record.url?scp=85046400146&partnerID=8YFLogxK
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U2 - 10.1109/TIFS.2018.2834232
DO - 10.1109/TIFS.2018.2834232
M3 - Article
AN - SCOPUS:85046400146
SN - 1556-6013
VL - 13
SP - 2995
EP - 3007
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
IS - 12
ER -