Obfuscation at-source: Privacy in context-aware mobile crowd-sourcing

• Main Authors: KANDAPPU, Thivya, MISRA, Archan, CHENG, Shih-Fen, TANDRIANSYAH, Randy, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Privacy; Mobile Crowd-sourcing platforms; obfuscation; trajectory; context-aware; Computer Sciences; Information Security; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3976; https://ink.library.smu.edu.sg/context/sis_research/article/4978/viewcontent/mobile_crowd_tasker_afv.pdf
• Institution: Singapore Management University

By effectively 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 efficiency 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 user-controlled obfuscation technique that provides a trade off-amenable framework that caters to multi-attribute privacy measures (considering the per-user sensitivity and global uniqueness of locations). We demonstrate the effectiveness of our approach by testing it using the real-world data collected from the well-established TA$Ker platform. More specifically, we show that one can increase its location entropy by 23% with only modest changes to the real trajectories while imposing an additional 24% (< 1 min) of detour overhead on average. Finally, we present insights derived by carefully inspecting various parameters that control the whole obfuscation process.