Differentiated Privacy-Preserving Task Assignment Scheme Based on Generative Adversarial Networks in Spatial Crowdsourcing

Abstract

The spatial crowdsourcing (SC) can quickly assign tasks and obtain feedback based on task requirements and workers’ locations, which brings great convenience to task assignment. However, sensitive information can also be easily obtained by SC platforms. To prevent information leakage, various privacy-preserving task assignment schemes have been proposed. However, existing schemes have low query efficiency and may leak pattern privacy, task content, or worker preference. To address the above challenges, this article proposes a differentiated privacy-preserving task assignment scheme based on generative adversarial networks in spatial crowdsourcing (DPGAN-SC). This scheme leverages generative adversarial networks (GANs) to generate disguised locations for both tasks and workers, which are then used in the task-matching process. Within the standard area range, no location can be distinguished, ensuring location privacy while preventing adversaries from analyzing search patterns through matching results. The combination of location disguise and task content encryption makes it impossible for adversaries to infer worker preferences and access patterns through the matching process. In addition, to meet differentiated privacy requirements, DPGAN-SC leverages GANs to design a three-level privacy-classification mechanism. This mechanism categorizes private data while minimizing unnecessary privacy overhead. Compared with existing schemes, DPGAN-SC improves query efficiency by 100 times while ensuring the comprehensive privacy preservation.