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• W2892565993 abstract "We introduce a tunable measure for information leakage called maximal alpha-leakage. This measure quantifies the maximal gain of an adversary in inferring any (potentially random) function of a dataset from a release of the data. The inferential capability of the adversary is, in turn, quantified by a class of adversarial loss functions that we introduce as $alpha$-loss, $alphain[1,infty]$. The choice of $alpha$ determines the specific adversarial action and ranges from refining a belief (about any function of the data) for $alpha=1$ to guessing the most likely value for $alpha=infty$ while refining the $alpha^{th}$ moment of the belief for $alpha$ in between. Maximal alpha-leakage then quantifies the adversarial gain under $alpha$-loss over all possible functions of the data. In particular, for the extremal values of $alpha=1$ and $alpha=infty$, maximal alpha-leakage simplifies to mutual information and maximal leakage, respectively. For $alphain(1,infty)$ this measure is shown to be the Arimoto channel capacity of order $alpha$. We show that maximal alpha-leakage satisfies data processing inequalities and a sub-additivity property thereby allowing for a weak composition result. Building upon these properties, we use maximal alpha-leakage as the privacy measure and study the problem of data publishing with privacy guarantees, wherein the utility of the released data is ensured via a hard distortion constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. We show that under a hard distortion constraint, for $alpha>1$ the optimal mechanism is independent of $alpha$, and therefore, the resulting optimal tradeoff is the same for all values of $alpha>1$. Finally, the tunability of maximal alpha-leakage as a privacy measure is also illustrated for binary data with average Hamming distortion as the utility measure." @default.
• W2892565993 created "2018-10-05" @default.
• W2892565993 creator A5048397143 @default.
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• W2892565993 date "2018-09-24" @default.
• W2892565993 modified "2023-09-23" @default.
• W2892565993 title "Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs" @default.
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• W2892565993 doi "https://doi.org/10.48550/arxiv.1809.09231" @default.
• W2892565993 hasPublicationYear "2018" @default.
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