Several Representations of $\alpha$-Mutual Information and Interpretations as Privacy Leakage Measures

In this paper, we present several novel representations of $\alpha$-mutual information ($\alpha$-MI) in terms of R{' e}nyi divergence and conditional R{' e}nyi entropy. The representations are based on the variational characterizations of $\alpha$-MI using a reverse channel. Based on these representations, we provide several interpretations of the $\alpha$-MI as privacy leakage measures using generalized mean and gain functions. Further, as byproducts of the representations, we propose novel conditional R{' e}nyi entropies that satisfy the property that conditioning reduces entropy and data-processing inequality.