An Alphabet of Leakage Measures
Atefeh Gilani, Gowtham R. Kurri, Oliver Kosut, Lalitha Sankar · 2022 IEEE Information Theory Workshop (ITW) · 2022
We introduce a family of information leakage measures called maximal α, β-leakage, parameterized by real numbers α and β. The measure is formalized via an operational definition involving an adversary guessing an unknown function of the data given the released data. We obtain a simple, computable expression for the measure and show that it satisfies several basic properties such as monotonicity in β for a fixed α, non-negativity, data processing inequalities, and additivity over independent releases. Finally, we highlight the relevance of this family by showing that it bridges several known leakage measures, including maximal α-leakage (β = 1), maximal leakage (α = ∞, β = 1), local differential privacy (α = ∞, β = ∞), and local Rényi differential privacy (α = β).