Privacy Under Hard Distortion Constraints
Privacy Under Hard Distortion Constraints
复制标题
硬失真约束下的隐私
DOI：
10.1109/itw.2018.8613385
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
F. Calmon
中科院分区：
文献类型：
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作者：
Jiachun Liao;O. Kosut;L. Sankar;F. Calmon
研究了具有隐私保证的数据公开问题，其中通过硬失真约束来保证公开数据的实用性。与平均失真不同，硬失真提供了保真度的确定性保证。对于隐私度量，我们使用可调的信息泄漏度量，即最大$\alpha$ -泄漏$(\alpha \in [1, \infty$，并制定隐私-效用权衡问题。由此产生的解决方案强调，在硬失真约束下，解决方案的性质对于本地和非本地隐私需求都保持不变。更准确地说，我们证明了最优机制和最优权衡对任何$\alpha \gt 1$都是不变的；即，可调泄漏度量仅表现为两个极值中的一个，即，对于$\alpha=1$是互信息，对于$\alpha=\infty$是最大泄漏。
We study the problem of data disclosure with privacy guarantees, wherein the utility of the disclosed data is ensured via a hard distortion constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. For the privacy measure, we use a tunable information leakage measure, namely maximal $\alpha$- leakage $(\alpha \in [1, \infty$ and formulate the privacy-utility tradeoff problem. The resulting solution highlights that under a hard distortion constraint, the nature of the solution remains unchanged for both local and non-local privacy requirements. More precisely, we show that both the optimal mechanism and the optimal tradeoff are invariant for any $\alpha \gt 1$; i.e., the tunable leakage measure only behaves as either of the two extrema, i.e., mutual information for $\alpha=1$ and maximal leakage for $\alpha=\infty$.