5 citations · 9 across the 4 of their papers we have counts for
8 papers
Three Variants of Differential Privacy: Lossless Conversion and Applications
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon +2
We consider three different variants of differential privacy (DP), namely approximate DP, Rényi DP (RDP), and hypothesis test DP. In the first part, we develop a machinery for opti…
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via -Divergences
Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon +2
We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of Rényi differential privacy (RDP). Our result is based on the joint range o…
Theoretical Guarantees for Model Auditing with Finite Adversaries
Mario Diaz, Peter Kairouz, Jiachun Liao +1
Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data. Yet, in practice, even models learned with privacy guarantees…
Robustness of Maximal -Leakage to Side Information
Jiachun Liao, Lalitha Sankar, Oliver Kosut +1
Maximal -leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter dete…
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…