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    Differentially private response mechanisms on categorical data


    Holohan, Naoise and Leith, Douglas J. and Mason, Oliver (2016) Differentially private response mechanisms on categorical data. Discrete Applied Mathematics, 211. pp. 86-98. ISSN 0166-218X

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    Abstract

    We study mechanisms for differential privacy on finite datasets. By deriving sufficient sets for differential privacy we obtain necessary and sufficient conditions for differential privacy, a tight lower bound on the maximal expected error of a discrete mechanism and a characterisation of the optimal mechanism which minimises the maximal expected error within the class of mechanisms considered.

    Item Type: Article
    Keywords: Data privacy; Differential privacy; Optimal mechanisms;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 10019
    Identification Number: https://doi.org/10.1016/j.dam.2016.04.010
    Depositing User: Oliver Mason
    Date Deposited: 27 Sep 2018 15:32
    Journal or Publication Title: Discrete Applied Mathematics
    Publisher: Elsevier
    Refereed: Yes
    Funders: Science Foundation Ireland (SFI)
    URI:

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