3 papers
stat.ML2025
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study
Eric Aubinais, Philippe Formont, Pablo Piantanida +1
Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance levels comparable to those of the origina…
cs.LG2023
Preserving Privacy in GANs Against Membership Inference Attack
Mohammadhadi Shateri, Francisco Messina, Fabrice Labeau +1
Generative Adversarial Networks (GANs) have been widely used for generating synthetic data for cases where there is a limited size real-world dataset or when data holders are unwil…
stat.ML2023
Fundamental Limits of Membership Inference Attacks on Machine Learning Models
Eric Aubinais, Elisabeth Gassiat, Pablo Piantanida
Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive information about individuals. This a…