Abstract
Location information is crucial for efficient task assignment in spatial crowdsourcing, but sharing such information raises privacy concerns. Differential privacy (DP) offers a solution by protecting location privacy while preserving data usefulness. Existing DP-based spatial crowdsourcing frameworks have two main limitations: 1) they fail to provide personalized privacy preservation for workers and 2) they prioritize incentive mechanisms (such as utility maximization and cost minimization) while overlooking quality control. To address these limitations, we formulate the multiobjective privacy-preserving task assignment (MP-TA) problem. This problem aims to maximize both incentives and quality while meeting service rate requirements and ensuring personalized privacy protection for workers. Accordingly, we present a three-phase framework comprising worker proposal, candidate worker selection, and task assignment optimization. To generate high-quality eligible solutions for both objectives, we introduce a distributed cooperative co-evolutionary multiobjective memetic algorithm (DCC-MMA) based on sequential subproblem division and knee-driven migration operation. Matching-based crossover, matching-based mutation, and fix operations are designed to enhance search efficiency. Experimental results demonstrate DCC-MMA's superiority in solution quality, convergence speed, and scalability compared to state-of-the-art algorithms.
