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    Privacy with Estimation Guarantees

    Wang, Hao and Vo, Lisa and Calmon, Flavio P. and Medard, Muriel and Duffy, Ken R. and Varia, Mayank (2019) Privacy with Estimation Guarantees. IEEE Transactions on Information Theory, 65 (12). pp. 8025-8042. ISSN 0018-9448

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    We study the central problem in data privacy: how to share data with an analyst while providing both privacy and utility guarantees to the user that owns the data. In this setting, we present an estimation-theoretic analysis of the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy). We demonstrate how chi-square information captures the fundamental PUT in this case and provide bounds for the best PUT. We propose a convex program to compute privacy-assuring mappings when the functions to be disclosed and hidden are known a priori and the data distribution is known. We derive lower bounds on the minimum mean-squared error of estimating a target function from the disclosed data and evaluate the robustness of our approach when an empirical distribution is used to compute the privacy-assuring mappings instead of the true data distribution. We illustrate the proposed approach through two numerical experiments.

    Item Type: Article
    Additional Information: This is the preprint version of the published article, which is available at H. Wang, L. Vo, F. P. Calmon, M. Médard, K. R. Duffy and M. Varia, "Privacy With Estimation Guarantees," in IEEE Transactions on Information Theory, vol. 65, no. 12, pp. 8025-8042, Dec. 2019, doi: 10.1109/TIT.2019.2934414.
    Keywords: Estimation; privacy-utility trade-off; minimum mean-squared error;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 13479
    Identification Number:
    Depositing User: Dr Ken Duffy
    Date Deposited: 03 Nov 2020 15:29
    Journal or Publication Title: IEEE Transactions on Information Theory
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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