TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
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Computer Science > Machine Learning
Title:TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
Abstract: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 first show that even basic membership inference attacks succeed against tabular ICL, motivating formal privacy protection. We then introduce TabPATE, a differentially private PATE-style defense for tabular ICL that does not require public in-distribution data. TabPATE partitions the private context across teacher models, privately aggregates their labels on synthetic tabular queries, and releases the resulting labeled queries as a student context. Because tabular features are bounded and relatively low-dimensional, useful queries can be generated from feature ranges alone or from lightly privatized marginals. Across tabular benchmarks, TabPATE preserves competitive utility while reducing membership inference to near-random success, providing a practical path to private tabular ICL without public data.
| Comments: | Presented at the 2nd ICML Workshop on Foundation Models for Structured Data (2026) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.31474 [cs.LG] |
| (or arXiv:2606.31474v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31474
arXiv-issued DOI via DataCite (pending registration)
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