Publication Type

Conference Proceeding Article

Publication Date

3-2017

Abstract

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.

Keywords

Daily routines, Effective location, In contexts, Mobile crowdsourcing, Mobility traces, Privacy invasions, Push-based, User location, Perturbation techniques

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems; Intelligent Systems and Decision Analytics

Publication

2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops: Kona, Big Island, Hawaii, March 13-17

First Page

231

Last Page

236

ISBN

9781509043385

Identifier

10.1109/PERCOMW.2017.7917563

Publisher

IEEE

City or Country

Piscataway, NJ

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/PERCOMW.2017.7917563

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