10 citations · 33 across the 12 of their papers we have counts for
12 papers
Anonymization, Not Elimination: Utility-Preserved Speech Anonymization
Yunchong Xiao, Yuxiang Zhao, Ziyang Ma +4
The growing reliance on large-scale speech data has made privacy protection a critical concern. However, existing anonymization approaches often degrade data utility, for example b…
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…
Generating Fair Universal Representations using Adversarial Models
Peter Kairouz, Jiachun Liao, Chong Huang +3
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our f…
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…
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…
Hypothesis Testing under Mutual Information Privacy Constraints in the High Privacy Regime
Jiachun Liao, Lalitha Sankar, Vincent Y. F. Tan +1
Hypothesis testing is a statistical inference framework for determining the true distribution among a set of possible distributions for a given dataset. Privacy restrictions may re…
Hypothesis Testing under Maximal Leakage Privacy Constraints
Jiachun Liao, Lalitha Sankar, Flavio P. Calmon +1
The problem of publishing privacy-guaranteed data for hypothesis testing is studied using the maximal leakage (ML) as a metric for privacy and the type-II error exponent as the uti…
Hypothesis Testing in the High Privacy Limit
Jiachun Liao, Lalitha Sankar, Vincent Y. F. Tan +1
Binary hypothesis testing under the Neyman-Pearson formalism is a statistical inference framework for distinguishing data generated by two different source distributions. Privacy r…