REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing

by   Zhikun Zhang, et al.

Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies on co-design of incentive mechanism and privacy preservation assume a trustworthy fusion center (FC). Very recent work has taken steps to relax the assumption on trustworthy FC and allows participatory users (PUs) to add well calibrated noise to their raw sensing data before reporting them, whereas the focus is on the equilibrium behavior of data subjects with binary data. Making a paradigm shift, this paper aim to quantify the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer adding larger noise for higher privacy-preserving levels (PPLs). To achieve a good balance therein, we design an efficient incentive mechanism to REconcile FC's Aggregation accuracy and individual PU's data Privacy (REAP). Specifically, we adopt the celebrated notion of differential privacy to measure PUs' PPLs and quantify 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 PUs. It can further overcome the information asymmetry, i.e., the 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.


page 1

page 2

page 3

page 4


LEPA: Incentivizing Long-term Privacy-preserving Data Aggregation in Crowdsensing

In this paper, we study the incentive mechanism design for real-time dat...

Context-aware Data Aggregation with Localized Information Privacy

In this paper, localized information privacy (LIP) is proposed, as a new...

Incentive Mechanism for Uncertain Tasks under Differential Privacy

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensin...

Incentive Mechanisms to Prevent Efficiency Loss of Non-Profit Utilities

The modernization of the power system introduces technologies that may i...

Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

With the rapid development of artificial intelligence (AI), ethical issu...

Incentives for Privacy Tradeoff in Community Sensing

Community sensing, fusing information from populations of privately-held...

The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition

While users claim to be concerned about privacy, often they do little to...

Please sign up or login with your details

Forgot password? Click here to reset