Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model
Name:
Privacy_Preservation_in_Location_Based_Services__A_Novel_Metric_and_Attack_Model.pdf
Size:
3.776Mb
Format:
PDF
Description:
Accepted manuscript
Type
ArticleDate
2020Permanent link to this record
http://hdl.handle.net/10754/662822
Metadata
Show full item recordAbstract
Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset.Citation
Shaham, S., Ding, M., Liu, B., Dang, S., Lin, Z., & Li, J. (2020). Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model. IEEE Transactions on Mobile Computing, 1–1. doi:10.1109/tmc.2020.2993599Publisher
IEEEarXiv
1805.06104Additional Links
https://ieeexplore.ieee.org/document/9090973/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9090973
http://arxiv.org/pdf/1805.06104
ae974a485f413a2113503eed53cd6c53
10.1109/TMC.2020.2993599