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    Privacy Preserving Location Data Publishing: A Machine Learning Approach

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    08951246.pdf
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    2.930Mb
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    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
    2020-01-07
    Online Publication Date
    2020-01-07
    Print Publication Date
    2021-09-01
    Permanent link to this record
    http://hdl.handle.net/10754/661021
    
    Metadata
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    Abstract
    Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.
    Citation
    Shaham, S., Ding, M., Liu, B., Dang, S., Lin, Z., & Li, J. (2020). Privacy Preserving Location Data Publishing: A Machine Learning Approach. IEEE Transactions on Knowledge and Data Engineering, 1–1. doi:10.1109/tkde.2020.2964658
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Knowledge and Data Engineering
    DOI
    10.1109/TKDE.2020.2964658
    arXiv
    1902.08934
    Additional Links
    https://ieeexplore.ieee.org/document/8951246/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8951246
    http://arxiv.org/pdf/1902.08934
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2020.2964658
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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