1 citations · 3 across the 4 of their papers we have counts for
6 papers
Privacy Amplification of Iterative Algorithms via Contraction Coefficients
Shahab Asoodeh, Mario Diaz, Flavio P. Calmon
We investigate the framework of privacy amplification by iteration, recently proposed by Feldman et al., from an information-theoretic lens. We demonstrate that differential privac…
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…
A Tunable Loss Function for Binary Classification
Tyler Sypherd, Mario Diaz, Lalitha Sankar +1
We present -loss, , a tunable loss function for binary classification that bridges log-loss () and - loss (). We prove that -loss has 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,…
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…
On the Noise-Information Separation of a Private Principal Component Analysis Scheme
Mario Diaz, Shahab Asoodeh, Fady Alajaji +3
In a survey disclosure model, we consider an additive noise privacy mechanism and study the trade-off between privacy guarantees and statistical utility. Privacy is approached from…