Privacy in context-aware mobile crowdsourcing systems

• Main Authors: KANDAPPU, Thivya, MISRA, Archan, CHENG, Shih-Fen, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Daily routines; Effective location; In contexts; Mobile crowdsourcing; Mobility traces; Privacy invasions; Push-based; User location; Perturbation techniques; Artificial Intelligence and Robotics; Information Security; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3630; https://ink.library.smu.edu.sg/context/sis_research/article/4632/viewcontent/IEEE_certified_version.pdf
• Institution: Singapore Management University

Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker’s daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning effort. However, the push-based systems are not without drawbacks. The major concern is the potential privacy invasion that could result from the disclosure of individual’s mobility traces to the crowd-sourcing platform. In this paper, we first demonstrate specific threats of continuous sharing of users locations in such push-based crowd-sourcing platforms. We then propose a simple yet effective location perturbation technique that obfuscates certain user locations to achieve privacy guarantees while not affecting the quality of the recommendations the system generates.We use the mobility traces data we obtained from our urban campus to show the trade-offs between privacy guarantees and the quality of the recommendations associated with the proposed solution. We show that obfuscating even 75% of the individual trajectories will affect the user to make another extra 1.8 minutes of detour while gaining 62.5% more uncertainty of his location traces.