Pseudonymizing sensitive data for LLMs without losing context

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She utilizes a token proxy method to pseudonymize sensitive data, allowing large language models to process information without exposing personal details. This approach retains the context and meaning of the original data, making it useful for training and testing language models. The method is explained in detail on Attic Security's blog, providing a solution for balancing data privacy and model accuracy.