Privacy-preserving multiobjective task assignment scheme with differential obfuscation in mobile crowdsensing

The Mobile crowdsensing (MCS) platforms perform optimal task assignments by collecting a large amount of user information to improve system performance and efficiency. However, the collection and analysis of users' data may jeopardize their privacy. Existing privacy protection methods of task assignment have rarely considered the comprehensive protection of user privacy and focus primarily on single objective constraint condition. To address this issue, we propose a privacy -preserving multiobjective task assignment scheme (PMTA) to enable participants to obfuscate their sensitive data by utilizing differential privacy techniques. Based on game theory, PMTA uses the non -dominated sorting genetic algorithm II (NSGA-II) to optimize multiple objectives that minimize the expected travel distance of selected workers and the cost of task publishers. PMTA performs multi -dimensional privacy preserving of sensor users under multi -constraint conditions without the need for any Trusted Third Party (TTP). Extensive experimental results verify the effectiveness and efficiency of our scheme. Particularly, PMTA is superior to the scheme that only considers a single objective, reducing 41.3% average travel distance and 64% average cost, and is also superior to Laplace's obfuscation scheme reducing 25.6% average travel distance and 63.3% average cost of expenditure.