PRPS: Privacy-Preserving and Reputation-Aware Participant Selection Scheme for Mobile Crowd Sensing

Abstract

As an emerging sensing paradigm, mobile crowd sensing(MCS) comprises a collection of mobile users that utilize their sensing devices to efficiently execute and send data contributions. However, the integration of privacy and reputation mechanisms(evaluating reliability) is crucial requirements for building secure and reliable MCS applications. Firstly, participants are assured that their privacy is preserved even if they contribute sensitive personal data. Secondly, the reputation mechanism allows the server to monitor participant behaviors and reliability, as biased or inaccurate contributions may demote the system quality, making it essential for the server to validate participants. Integrating a reputation mechanism with privacy is a challenging and contradictory objective. The reputation mechanism measures the participant behavior during the entire sensing activity, while privacy aims to preserve participant credentials. Thus, a novel privacy-preserving and reputation-aware participant selection(PRPS) scheme for MCS has been proposed. The PRPS scheme integrates privacy with a reputation mechanism, preserves the privacy of participant identities and reputation values by employing pseudonyms and cloaking techniques, respectively, and protects the location and data privacy. Extensive simulations have been conducted. Using performance evaluation, we affirm precision, efficacy and scalability of the PRPS scheme by comparing privacy-preserving and utility-aware participant selection(PUPS) and utility-aware participant selection(UPS) schemes, respectively, and demonstrate the impact of privacy and reputation on data contributions. Next, the outcomes of the PRPS scheme are assessed. Finally, we estimate the efficiency and the accuracy of the PRPS scheme in evaluating participant reliability and behavior.

Keywords

mobile crowd sensing(MCS) / reputation / privacy / pseudonym / cloaking

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