Privacy-preserving personal sensitive data in crowdsourcing

Abstract

Spatial crowdsourcing system refers to sending various location-based tasks to workers according to their positions, and workers need to physically move to specified locations to accomplish tasks. The workers are restricted to report their real-time sensitive position to the server so as to keep in coordination with the crowdsourcing server. Therefore, implementing crowdsourcing system while preserving the privacy of workers sensitive information is a key issue that needs to be tackled. We discard the assumption of a trustworthy third party cellular service provider (CSP), and further propose a local method to achieve acceptable results. A differential privacy model ensures rigorous privacy guarantee, and Laplace mechanism noise is introduced to preserve workers sensitive information. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real-world datasets.