Privacy Preserving and Quality Aware Incentive Mechanism for Mobile Crowd Sensing
Author(s):
Patel Akhila
Keywords:
Abstract
Providing excellent economic rewards is an environment friendly way for cell crowd sensing to encourage the participation of venture participants. However, a financial incentive mechanism is commonly difficult to forestall malicious project contributors and a dishonest assignment requester. Moreover, prior quality-aware incentive schemes are commonly failed to hold the privatizes of project participants. Meanwhile, most current privacy-preserving incentive schemes bypass the facts exceptional of challenge participants. To address these issues, we recommend a privacy-preserving and facts quality-aware incentive scheme, referred to as PACE. In particular, facts satisfactory consist of the reliability and deviation of data. Specifically, we first suggest a zero-knowledge mannequin of facts reliability estimation that can defend information privatives whilst assessing statistics reliability. Then, we quantify the facts pleasant primarily based on the deviation between dependable information and the floor truth. Finally, we distribute economic rewards to undertaking contributors in accordance to their facts quality. To exhibit the effectiveness and effectively of PACE, we consider it in a real-world dataset. The assessment and evaluation effects exhibit that PACE can forestall malicious behaviors of undertaking members and a project requester, and achieves each privacy-preserving and information exceptional dimension of mission members.
Article Details
Unique Paper ID: 157274

Publication Volume & Issue: Volume 9, Issue 6

Page(s): 801 - 804
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