Research Article
An Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level
@INPROCEEDINGS{10.1007/978-3-031-73699-5_4, author={Qing Cao and Yunfei Tan and Guozheng Zhang}, title={An Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level}, proceedings={Security and Privacy in New Computing Environments. 6th International Conference, SPNCE 2023, Guangzhou, China, November 25--26, 2023, Proceedings}, proceedings_a={SPNCE}, year={2025}, month={1}, keywords={mobile crowdsensing incentive mechanism trajectory publication privacy protection differential privacy}, doi={10.1007/978-3-031-73699-5_4} }- Qing Cao
Yunfei Tan
Guozheng Zhang
Year: 2025
An Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level
SPNCE
Springer
DOI: 10.1007/978-3-031-73699-5_4
Abstract
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.
