论文标题
强渐近组成定理用于相互信息测量
Strong Asymptotic Composition Theorems for Mutual Information Measures
论文作者
论文摘要
我们表征了Sibson和Arimoto相互信息的增长以及$α$ - 最大的泄漏，即至少是统一的秩序，在随机变量和一组不断增长的嘈杂，有条件独立且相同分布的随机变量的观察结果之间。这些度量中的每一个都将指数迅速增加到订单和度量依赖性的极限，其指数是订单和量度无关的。
We characterize the growth of the Sibson and Arimoto mutual informations and $α$-maximal leakage, of any order that is at least unity, between a random variable and a growing set of noisy, conditionally independent and identically-distributed observations of the random variable. Each of these measures increases exponentially fast to a limit that is order- and measure-dependent, with an exponent that is order- and measure-independent.
