5 citations · 9 across the 12 of their papers we have counts for
14 papers
Reliable Estimation of Differential Privacy under Selection Bias
Valentin Dorseuil, Meenatchi Sundaram Muthu Selva Annamalai, Jamal Atif +2
Statistical privacy audits are essential for verifying the theoretical privacy guarantees of differentially private (DP) mechanisms. Auditors typically use Membership Inference Att…
Tight Auditing of Differential Privacy in MST and AIM
Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Bogdan Kulynych
State-of-the-art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarantees remains challenging. We in…
CLIOPATRA: Extracting Private Information from LLM Insights
Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro, Peter Kairouz
The widespread adoption of AI assistants has encouraged the development of privacy-aware platforms designed to extract insights from real-world usage. Their privacy protections pri…
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…
To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
Meenatchi Sundaram Muthu Selva Annamalai, Borja Balle, Jamie Hayes +1
The Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm supports the training of machine learning (ML) models with formal Differential Privacy (DP) guarantees. Tr…
The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging
Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro
Synthetic data created by differentially private (DP) generative models is increasingly used in real-world settings. In this context, PATE-GAN has emerged as one of the most popula…
"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation
Meenatchi Sundaram Muthu Selva Annamalai, Georgi Ganev, Emiliano De Cristofaro
Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing…
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro
This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter…
FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting
Meenatchi Sundaram Muthu Selva Annamalai, Igor Bilogrevic, Emiliano De Cristofaro
Browser fingerprinting often provides an attractive alternative to third-party cookies for tracking users across the web. In fact, the increasing restrictions on third-party cookie…
A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data
Meenatchi Sundaram Muthu Selva Annamalai, Andrea Gadotti, Luc Rocher
Recent advances in synthetic data generation (SDG) have been hailed as a solution to the difficult problem of sharing sensitive data while protecting privacy. SDG aims to learn sta…