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• W2890215720 abstract "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." @default.
• W2890215720 created "2018-09-27" @default.
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• W2890215720 date "2017-10-01" @default.
• W2890215720 modified "2023-09-26" @default.
• W2890215720 title "Privacy with Estimation Guarantees" @default.
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• W2890215720 doi "https://doi.org/10.48550/arxiv.1710.00447" @default.
• W2890215720 hasPublicationYear "2017" @default.
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