PACE: Privacy-preserving and quality-aware incentive mechanism for mobile crowdsensing

Publication Type

Journal Article

Publication Date

1-2020

Abstract

Providing appropriate monetary rewards is an efficient way for mobile crowdsensing to motivate the participation of task participants. However, a monetary incentive mechanism is generally challenging to prevent malicious task participants and a dishonest task requester. Moreover, prior quality-aware incentive schemes are usually failed to preserve the privacy of task participants. Meanwhile, most existing privacy-preserving incentive schemes ignore the data quality of task participants. To tackle these issues, we propose a privacy-preserving and data quality-aware incentive scheme, called PACE. In particular, data quality consists of the reliability and deviation of data. Specifically, we first propose a zero-knowledge model of data reliability estimation that can protect data privacy while assessing data reliability. Then, we quantify the data quality based on the deviation between reliable data and the ground truth. Finally, we distribute monetary rewards to task participants according to their data quality. To demonstrate the effectiveness and efficiency of PACE, we evaluate it in a real-world dataset. The evaluation and analysis results show that PACE can prevent malicious behaviors of task participants and a task requester, and achieves both privacy-preserving and data quality measurement of task participants.

Keywords

quality-aware, zero-knowledge, incentive mechanism, mobile crowdsensing, privacy-preserving

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Mobile Computing

Volume

20

Issue

5

First Page

1924

Last Page

1939

ISSN

1536-1233

Identifier

10.1109/TMC.2020.2973980

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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