Operational definitions for some common information leakage metrics
Operational definitions for some common information leakage metrics
复制标题
一些常见信息泄露指标的操作定义
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Aaron B. Wagner
中科院分区：
文献类型：
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作者：
Ibrahim Issa;Aaron B. Wagner
从随机变量 X 到随机变量 Y 的最大泄漏定义为，在观察 Y 时，正确猜测 X 的随机函数的概率的乘法增长，在所有此类函数中最大化 [1]。在此，该猜测框架用于对常见信息泄漏指标给出操作定义，包括香农容量、最大相关性和局部差分隐私。香农容量被证明可以捕获正确猜测 X 的受限函数集的概率的乘法增长，这些函数可以从 Y 可靠地重建，从而低估泄漏。最大相关性用于捕获 X 函数方差的乘法变化，而不是猜测概率。局部差分隐私被证明可以捕获 X 函数的猜测概率的乘法增长，在 Y 的实现和分布 Px 上最大化。此外，对于固定 Px，最大化 Y 的实现可以产生有效的泄漏测量，该测量等于最大信息率。
Maximal leakage from a random variable X to a random variable Y is defined as the multiplicative increase, upon observing Y, of the probability of correctly guessing a randomized function of X, maximized over all such functions [1]. Herein, this guessing framework is used to give operational definitions to common information leakage metrics, including Shannon capacity, maximal correlation, and local differential privacy. Shannon capacity is shown to capture the multiplicative increase of the probability of correct guessing over the restricted set of functions of X that can be reliably reconstructed from Y, hence underestimating leakage. Maximal correlation is shown to capture the multiplicative change in the variance of functions of X, rather than the guessing probability. Local differential privacy is shown to capture the multiplicative increase of the guessing probability of functions of X, maximized over realizations of Y and over distributions Px. Moreover, maximizing over realizations of Y for a fixed Px is shown to yield a valid leakage measure, which is equal to the maximum information rate.