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Found 3 papers, 1 papers with code
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Lin et al. (2024) recently introduced the Private Evolution (PE) algorithm to generate DP synthetic images with only API access to diffusion models.
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks.
Differentially Private Model Compression
Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models for many downstream Natural Language Processing (NLP) tasks while simultaneously guaranteeing differential privacy.
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