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.

翻译：对于函数 $\tilde{f}(t) = \exp(\frac{\alpha-1}{\alpha}t)$，本文提出了一种 $\tilde{f}$-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 $y$ 上引起的最大 $\tilde{f}$-均值信息增益，而Sibson互信息则是该 $Y$ 基本信息增益的 $\tilde{f}$-均值。二者均被提出作为α泄漏度量，用以表征攻击者能够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泄漏可通过缩放概率表示为 $\tilde{f}$-均值度量。此外，Sibson互信息可解释为对信道输出施加所有估计决策所能获得的最大 $\tilde{f}$-均值信息增益。