For $\tilde{f}(t) = \exp(\frac{\alpha-1}{\alpha}t)$, this paper proposes a $\tilde{f}$-mean information gain measure. R\'{e}nyi divergence is shown to be the maximum $\tilde{f}$-mean information gain incurred at each elementary event $y$ of channel output $Y$ and Sibson mutual information is the $\tilde{f}$-mean of this $Y$-elementary information gain. Both are proposed as $\alpha$-leakage measures, indicating the most information an adversary can obtain on sensitive data. It is shown that the existing $\alpha$-leakage by Arimoto mutual information can be expressed as $\tilde{f}$-mean measures by a scaled probability. Further, Sibson mutual information is interpreted as the maximum $\tilde{f}$-mean information gain over all estimation decisions applied to channel output. This reveals that the exiting generalized Blahut-Arimoto method for computing R\'{e}nyi capacity (or Gallager's error exponent) in fact maximizes a $\tilde{f}$-mean information gain iteratively over estimation decision and channel input. This paper also derives a decomposition of $\tilde{f}$-mean information gain, analogous to the Sibson identity for R\'{e}nyi divergence.

翻译：对于̃f(t) = exp((α-1)/α t)，本文提出一种̃f-均值信息增益度量。研究表明，Rényi散度是信道输出Y的每个基本事件y上产生的最大̃f-均值信息增益，而Sibson互信息则是该Y-基本信息增益的̃f-均值。两者均被提出作为α-泄露度量，用于衡量攻击者可能从敏感数据中获取的最大信息量。本文证明，现有基于Arimoto互信息的α-泄露可通过缩放概率表示为̃f-均值度量形式。进一步地，Sibson互信息被解释为对所有应用于信道输出的估计决策的最大̃f-均值信息增益。这一发现揭示了现有用于计算Rényi容量（或Gallager误差指数）的广义Blahut-Arimoto方法，实质上是在估计决策与信道输入之间迭代地最大化某一̃f-均值信息增益。本文还推导出̃f-均值信息增益的分解形式，该分解与Sibson关于Rényi散度的恒等式相类似。