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Providing privacy-aware incentives for mobile sensing

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2 Author(s)
Qinghua Li ; Department of Computer Science and Engineering, The Pennsylvania State University ; Guohong Cao

Mobile sensing exploits data contributed by mobile users (e.g., via their smart phones) to make sophisticated inferences about people and their surrounding and thus can be applied to environmental monitoring, traffic monitoring and healthcare. However, the large-scale deployment of mobile sensing applications is hindered by the lack of incentives for users to participate and the concerns on possible privacy leakage. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two privacy-aware incentive schemes for mobile sensing to promote user participation. These schemes allow each mobile user to earn credits by contributing data without leaking which data it has contributed, and at the same time ensure that dishonest users cannot abuse the system to earn unlimited amount of credits. The first scheme considers scenarios where a trusted third party (TTP) is available. It relies on the TTP to protect user privacy, and thus has very low computation and storage cost at each mobile user. The second scheme removes the assumption of TTP and applies blind signature and commitment techniques to protect user privacy.

Published in:

Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on

Date of Conference:

18-22 March 2013