70 citations · 293 across the 70 of their papers we have counts for
8 papers · 1 filter
Correspondence Analysis of Government Expenditure Patterns
Hsiang Hsu, Flavio P. Calmon, José Cândido Silveira Santos Filho +2
We analyze expenditure patterns of discretionary funds by Brazilian congress members. This analysis is based on a large dataset containing over million expenses made publicly a…
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,…
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…
Generalizing Correspondence Analysis for Applications in Machine Learning
Hsiang Hsu, Salman Salamatian, Flavio P. Calmon
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random vari…
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…
Generalizing Bottleneck Problems
Hsiang Hsu, Shahab Asoodeh, Salman Salamatian +1
Given a pair of random variables and two convex functions and , we introduce two bottleneck functionals as the lower and upper boundaries of the two-d…
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…
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning
Hao Wang, Berk Ustun, Flavio P. Calmon
In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive at…