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Generating synthetic data with differentially private LLM inference
Differential privacy (DP) provides mathematically rigorous guarantees that an algorithm will not reveal details about an individual’s data. However, endowing algorithms with DP guarantees can add complexity to an already complex machine learning (ML) pipeline. This is especially true at the scale of modern ML, where pipelines are...
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