
The author explores the use of differential privacy to protect tabular data in the context of in-context learning, proposing two frameworks - LDP-TabICL and GDP-TabICL - that offer privacy guarantees while maintaining performance.


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dp-tabicl-in-context-learning-with-differentially-private-tabular-data


DP-TabICL: In-Context Learning with Differentially Private Tabular Data


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Differential privacy mechanisms can protect tabular data in in-context learning, ensuring privacy while maintaining performance.
