5 citations · 9 across the 12 of their papers we have counts for
5 papers · 2 filters
A Unified Framework for Adversary-Aware Differential Privacy Bounds
Marika Swanberg, Meenatchi Sundaram Muthu Selva Annamalai, Jamie Hayes +2
Differential Privacy (DP) bounds the privacy leakage of a mechanism against worst-case membership inference, but the precise tradeoff between complex adversarial models and DP prot…
The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes +2
In this paper, we systematize research on auditing Differential Privacy (DP) techniques, aiming to identify key insights and open challenges. First, we introduce a comprehensive fr…
Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou +1
Privacy attacks, particularly membership inference attacks (MIAs), are widely used to assess the privacy of generative models for tabular synthetic data, including those with Diffe…
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Sofiane Mahiou +1
Differentially Private (DP) generative marginal models are often used in the wild to release synthetic tabular datasets in lieu of sensitive data while providing formal privacy gua…
Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions
Meenatchi Sundaram Muthu Selva Annamalai, Igor Bilogrevic, Emiliano De Cristofaro
Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research on the prevalence of fingerprinting h…