4 papers · 1 filter
Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models
Dariush Wahdany, Jesse Cresswell, Naiqing Guan +3
Tabular foundation models (TFMs) are increasingly deployed through black-box APIs and trained on large collections of real-world tabular datasets. As this training paradigm becomes…
TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
Dariush Wahdany, Matthew Jagielski, Jesse C. Cresswell +2
Tabular foundation models enable accurate in-context learning (ICL) from small labeled datasets, but the private records placed in context can leak through model predictions. We fi…
Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic +1
In machine learning, curation is used to select the most valuable data for improving both model accuracy and computational efficiency. Recently, curation has also been explored as…
Differentially Private Prototypes for Imbalanced Transfer Learning
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic +1
Machine learning (ML) models have been shown to leak private information from their training datasets. Differential Privacy (DP), typically implemented through the differential pri…