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20212026
most citedPANORAMIA: Privacy Auditing of Machine Learning Models without Retraining

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cs.CR2026

Neighborhood Watch: Privacy Risks in Seeded Local Combination Synthetic Data

Hadrien Lautraite, Tristan Allard, Anne-Sophie Charest +2

Synthetic data is seen as a promising solution for sharing data in sensitive contexts. However, recent work on privacy attacks have shown that there are still significant residual…

cs.LG2026

Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data

Maryam Babaei, Yingke Wang, Hadrien Lautraite +3

Counterfactuals are typically used in high-stakes decision areas to explain a machine learning model by showing how changes to the user profiles result in the desired outcome. Howe…