most citedFundamental Limits of Membership Inference Attacks on Machine Learning Models

2 citations · 2 across the 2 of their papers we have counts for

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2 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…

stat.ML2023★ 2 cited

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…