Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Abstract

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals in collecting and analyzing environmental, social, and other spatio-temporal information. SC outsources spatio-temporal tasks to a set of workers , i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations. However, current solutions require the workers to disclose their locations to untrusted parties. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Experimental results on real-world data show that the proposed technique protects location privacy without incurring significant performance overhead.