AIBearisharXiv – CS AI · 6h ago7/10
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Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries
Researchers demonstrate that transformer-based tabular foundation models leak sensitive information through their attention mechanisms, enabling effective membership inference attacks despite being pre-trained on synthetic data. The study proposes both an attack method (AMIA) and a defense strategy inspired by k-anonymity that reduces privacy leakage by 50% while maintaining model performance.