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- W2951191789 abstract "This paper investigates the relation between three different notions of privacy: identifiability, differential privacy and mutual-information privacy. Under a unified privacy-distortion framework, where the distortion is defined to be the Hamming distance of the input and output databases, we establish some fundamental connections between these three privacy notions. Given a distortion level $D$, define $epsilon_{mathrm{i}}^*(D)$ to be the smallest (best) identifiability level, and $epsilon_{mathrm{d}}^*(D)$ to be the smallest differential privacy level. We characterize $epsilon_{mathrm{i}}^*(D)$ and $epsilon_{mathrm{d}}^*(D)$, and prove that $epsilon_{mathrm{i}}^*(D)-epsilon_Xleepsilon_{mathrm{d}}^*(D)leepsilon_{mathrm{i}}^*(D)$ for $D$ in some range, where $epsilon_X$ is a constant depending on the distribution of the original database $X$, and diminishes to zero when the distribution of $X$ is uniform. Furthermore, we show that identifiability and mutual-information privacy are consistent in the sense that given distortion level $D$, the mechanism that optimizes the mutual-information privacy also minimizes the identifiability level." @default.
- W2951191789 created "2019-06-27" @default.
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- W2951191789 date "2014-02-16" @default.
- W2951191789 modified "2023-10-16" @default.
- W2951191789 title "On the Relation Between Identifiability, Differential Privacy and Mutual-Information Privacy" @default.
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- W2951191789 doi "https://doi.org/10.48550/arxiv.1402.3757" @default.
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