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    Local and global recoding methods for anonymizing set-valued data

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    Type
    Article
    Authors
    Terrovitis, Manolis
    Mamoulis, Nikos
    Kalnis, Panos cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2010-06-10
    Online Publication Date
    2010-06-10
    Print Publication Date
    2011-02
    Permanent link to this record
    http://hdl.handle.net/10754/561606
    
    Metadata
    Show full item record
    Abstract
    In this paper, we study the problem of protecting privacy in the publication of set-valued data. Consider a collection of supermarket transactions that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve as links to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the set. Unlike most previous works, we do not distinguish data as sensitive and non-sensitive, but we consider them both as potential quasi-identifiers and potential sensitive data, depending on the knowledge of the adversary. We define a new version of the k-anonymity guarantee, the k m-anonymity, to limit the effects of the data dimensionality, and we propose efficient algorithms to transform the database. Our anonymization model relies on generalization instead of suppression, which is the most common practice in related works on such data. We develop an algorithm that finds the optimal solution, however, at a high cost that makes it inapplicable for large, realistic problems. Then, we propose a greedy heuristic, which performs generalizations in an Apriori, level-wise fashion. The heuristic scales much better and in most of the cases finds a solution close to the optimal. Finally, we investigate the application of techniques that partition the database and perform anonymization locally, aiming at the reduction of the memory consumption and further scalability. A thorough experimental evaluation with real datasets shows that a vertical partitioning approach achieves excellent results in practice. © 2010 Springer-Verlag.
    Citation
    Terrovitis, M., Mamoulis, N., & Kalnis, P. (2010). Local and global recoding methods for anonymizing set-valued data. The VLDB Journal, 20(1), 83–106. doi:10.1007/s00778-010-0192-8
    Sponsors
    We would like to thank the authors of [12] for sharing with us the implementation of the Partition algorithm. This work was supported by grant HKU 715108E from Hong Kong RGC.
    Publisher
    Springer Nature
    Journal
    The VLDB Journal
    DOI
    10.1007/s00778-010-0192-8
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00778-010-0192-8
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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