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Found 2 papers, 1 papers with code
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs and it has been shown to have comparable performance to costly model retraining and fine-tuning.
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before the data is collected by a central server for model training.
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