Personalized Privacy-Preserving Task Allocation in Spatial Crowdsourcing

As a popular service management system, the spatial crowdsourcing (SC) server is responsible for allocating nearby workers to perform tasks based on outsourced locations. However, protecting the sensitive information contained in these outsourced locations is crucial. Traditional differential privacy (DP) methods suffer from two limitations: 1) they usually rely on a trusted third party, failing to protect both worker and task location privacy simultaneously, thus risking privacy breaches; 2) they ignore the personalized privacy demands of different users. In this paper, we propose a personalized local DP-based location obfuscation (PLDPLO) scheme, thereby providing personalized privacy-preserving both worker and task locations locally while allocating high-quality tasks. To achieve this, we introduce a personalized location indistinguishability (PLI) model, a new personalized Laplace mechanism achieving local DP, to jointly provide the protection of worker locations and different privacy levels for different workers. To address task privacy, we present a spatial mapping indistinguishability (SMI) algorithm to obfuscate task locations based on a random response mechanism, thereby ensuring data utility. Additionally, we propose a Zipf-Poisson model-based task allocation graph (ZPTAG) algorithm to perform one-task-multiple-workers allocation and achieve a high competitive ratio, which reduces the move distance of workers. Our PLDPLO scheme guarantees ϵ-LDP. Extensive experiments over real datasets demonstrate that our scheme achieves over 89% data utility for task allocation and outperforms state-of-the-art methods while providing personalized privacy levels.