Publication Type

Journal Article

Publication Date

3-2018

Abstract

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% (

Keywords

Privacy, Mobile Crowd-sourcing platforms, obfuscation, trajectory, context-aware

Discipline

OS and Networks | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Volume

2

Issue

1

ISSN

2474-9567

Identifier

10.1145/3191748

Publisher

Association for Computing Machinery (ACM)

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/10.1145/3191748

Share

COinS