Obfuscation at-source: Privacy in context-aware mobile crowd-sourcing
Abstract
By e!ectively reaching out to and engaging larger population of mobile users, mobile crowd-sourcing has become a strategy to perform large amount of urban tasks. The recent empirical studies have shown that compared to the pull-based approach, which expects the users to browse through the list of tasks to perform, the push-based approach that actively recommends tasks can greatly improve the overall system performance. As the e"ciency of the push-based approach is achieved by incorporating worker’s mobility traces, privacy is naturally a concern. In this paper, we propose a novel, 2-stage and usercontrolled obfuscation technique that provides a tradeo!-amenable framework that caters to multi-attribute privacy measures (considering the per-user sensitivity and global uniqueness of locations). We demonstrate the e!ectiveness of our approach by testing it using the real-world data collected from the well-established TA$Ker platform. More speci#cally, we show that one can increase its location entropy by 23% with only modest changes to the real trajectories while imposing an additional 24% (