388 citations · 1.5k across the 94 of their papers we have counts for
6 papers · 2 filters
On the Robustness of Information-Theoretic Privacy Measures and Mechanisms
Mario Diaz, Hao Wang, Flavio P. Calmon +1
Consider a data publishing setting for a dataset composed by both private and non-private features. The publisher uses an empirical distribution, estimated from i.i.d. samples,…
Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs
Jiachun Liao, Oliver Kosut, Lalitha Sankar +1
We introduce a tunable measure for information leakage called maximal alpha-leakage. This measure quantifies the maximal gain of an adversary in inferring any (potentially random)…
A Tunable Measure for Information Leakage
Jiachun Liao, Oliver Kosut, Lalitha Sankar +1
A tunable measure for information leakage called \textit{maximal -leakage} is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version o…
Privacy Under Hard Distortion Constraints
Jiachun Liao, Oliver Kosut, Lalitha Sankar +1
We study the problem of data disclosure with privacy guarantees, wherein the utility of the disclosed data is ensured via a \emph{hard distortion} constraint. Unlike average distor…
On the Contractivity of Privacy Mechanisms
Mario Diaz, Lalitha Sankar, Peter Kairouz
We present a novel way to compare the statistical cost of privacy mechanisms using their Dobrushin coefficient. Specifically, we provide upper and lower bounds for the Dobrushin co…
The Utility Cost of Robust Privacy Guarantees
Hao Wang, Mario Diaz, Flavio P. Calmon +1
Consider a data publishing setting for a data set with public and private features. The objective of the publisher is to maximize the amount of information about the public feature…