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    Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model

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    Name:
    Privacy_Preservation_in_Location_Based_Services__A_Novel_Metric_and_Attack_Model.pdf
    Size:
    3.776Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Shaham, Sina
    Ding, Ming
    Liu, Bo
    Dang, Shuping
    Lin, Zihuai
    Li, Jun
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/662822
    
    Metadata
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    Abstract
    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.2020.2993599
    Publisher
    IEEE
    Journal
    IEEE Transactions on Mobile Computing
    DOI
    10.1109/TMC.2020.2993599
    arXiv
    1805.06104
    Additional 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
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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