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    Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification

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    Author
    Farokhi, F
    Date
    2020
    Source Title
    IEEE Transactions on Big Data
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    University of Melbourne Author/s
    Farokhi, Farhad
    Affiliation
    Electrical and Electronic Engineering
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Farokhi, F. (2020). Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification. IEEE Transactions on Big Data, PP (99), https://doi.org/10.1109/tbdata.2019.2963391.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251371
    DOI
    10.1109/tbdata.2019.2963391
    Abstract
    We consider the problem of publicly releasing a dataset for support vector machine classification while not infringing on the privacy of data subjects (i.e., individuals whose private information is stored in the dataset). The dataset is systematically obfuscated using an additive noise for privacy protection. Motivated by the Cramér-Rao bound, inverse of the trace of the Fisher information matrix is used as a measure of the privacy. Conditions are established for ensuring that the classifier extracted from the original dataset and the obfuscated one are close to each other (capturing the utility). The optimal noise distribution is determined by maximizing a weighted sum of the measures of privacy and utility. The optimal privacy-preserving noise is proved to achieve local differential privacy. The results are generalized to a broader class of optimization-based supervised machine learning algorithms. Applicability of the methodology is demonstrated on multiple datasets.

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