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    Non-stochastic hypothesis testing for privacy

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    Author
    Farokhi, F
    Date
    2020-11-01
    Source Title
    IET Information Security
    Publisher
    Institution of Engineering and Technology
    University of Melbourne Author/s
    Farokhi, Farhad
    Affiliation
    Electrical and Electronic Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Farokhi, F. (2020). Non-stochastic hypothesis testing for privacy. IET Information Security, 14 (6), pp.754-763. https://doi.org/10.1049/iet-ifs.2020.0223.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251360
    DOI
    10.1049/iet-ifs.2020.0223
    Abstract
    In this paper, we consider privacy against hypothesis testing adversaries within a non-stochastic framework. We develop a theory of non-stochastic hypothesis testing by borrowing the notion of uncertain variables from non-stochastic information theory. We define tests as binary-valued mappings on uncertain variables and prove a fundamental bound on the best performance of tests in non-stochastic hypothesis testing. We provide parallels between stochastic and non-stochastic hypothesis-testing frameworks. We use the performance bound in non-stochastic hypothesis testing to develop a measure of privacy. We then construct reporting policies with prescribed privacy and utility guarantees. The utility of a reporting policy is measured by the distance between the reported and original values. Finally, we present the notion of indistinguishability as a measure of privacy by extending identifiability from the privacy literature to the non-stochastic framework. We prove that linear quantizers can indeed achieve identifiability for responding to linear queries on private datasets.

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