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RATE: Privacy-Preserving Task Assignment With Bi-Objective Optimization for Mobile Crowdsensing
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AI
DOI：10.1109/TMC.2024.3439584.png)
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Abstract
En 中文
Assigning sensing tasks to appropriate task participants is critical for mobile crowdsensing (MCS) and is an essential optimization problem. However, existing task assignment (or say participant selection) solutions for MCS generally support a single-objective optimization (e.g., minimizing travel distance or maximizing social welfare). Additionally, task assignment for MCS usually requires task participants' and a task requester's location information, which compromises their location privacy and hinders participation willingness. To achieve task assignment with bi-objective optimization and safeguard bilateral privacy, in this paper, we propose RATE, a privacy-preserving task assignment with bi-objective optimization for MCS. RATE features the following characteristics. First, RATE enables task assignment with bi-objective optimization including maximizing the social welfare and the requester's revenue, simultaneously. Second, RATE achieves bilateral privacy-preserving task assignment with bi-objective optimization by carefully designing underlyingly secure computing protocols. Third, RATE approximates optimal results of task assignments without sacrificing privacy. Theoretical analyses show that RATE protects the location privacy of both the task requester and the task participants. Meanwhile, experimental evaluations demonstrate that RATE outperforms traditional task assignment solutions and generates the task assignment result effectively and efficiently.
Keywords:
Task analysis
Privacy
Optimization
Sensors
Protection
Protocols
Costs
Bi-objective optimization
crowdsensing
privacy protection
secure computing
task assignment
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