Lalitha Sankar

A Tunable Measure for Information Leakage

2018 · arXiv (Cornell University)

A tunable measure for information leakage called \textit{maximal $α$-leakage} is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of …

An Operational Approach to Information Leakage via Generalized Gain Functions

2022 · arXiv (Cornell University)

We introduce a \emph{gain function} viewpoint of information leakage by proposing \emph{maximal $g$-leakage}, a rich class of operationally meaningful leakage measures that subsumes recently introduced leakage measures -- {maximal leakage} and {maximal $α$-leakage}. In maximal …

Optimizing Noise Distributions for Differential Privacy

2025 · arXiv (Cornell University)

We propose a unified optimization framework for designing continuous and discrete noise distributions that ensure differential privacy (DP) by minimizing Rényi DP, a variant of DP, under a cost constraint. Rényi DP has the advantage …