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In response to the incentive mechanism design issue and privacy leakage risk of trajectory data release in mobile crowdsensing scenario, an incentive mechanism named MSASM for participant selection is firstly presented in the paper, which considers the constraints of maximizing sensing area based on similarity measurement and utilizes a greedy and knapsack mixed algorithm to select the optimal participant set. Then an offline differentially private trajectory publishing algorithm named DPOTCPA is designed, which compresses the trajectory of participants and adds Laplace noise into the compressed trajectories for publishing. Experimental results on a real dataset demonstrate the effectiveness of the MSASM mechanism and the DPOTCPA algorithm.

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An Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level

  • 会议论文

  • 发表日期：

    2025年

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  • Qing Cao,
  • Yunfei Tan,
  • Guozheng Zhang

摘要

In response to the incentive mechanism design issue and privacy leakage risk of trajectory data release in mobile crowdsensing scenario, an incentive mechanism named MSASM for participant selection is firstly presented in the paper, which considers the constraints of maximizing sensing area based on similarity measurement and utilizes a greedy and knapsack mixed algorithm to select the optimal participant set. Then an offline differentially private trajectory publishing algorithm named DPOTCPA is designed, which compresses the trajectory of participants and adds Laplace noise into the compressed trajectories for publishing. Experimental results on a real dataset demonstrate the effectiveness of the MSASM mechanism and the DPOTCPA algorithm.