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This paper introduces Privacy-Preserving Fine-Tuning (PPFT), a novel training pipeline that allows LLMs to perform inference using embeddings of prompts instead of raw text, mitigating privacy risks associated with transmitting sensitive information. PPFT trains a client-side encoder and server-side projection module alongside the LLM, enabling the server to condition on k-pooled prompt embeddings. The projection module and LLM are then fine-tuned on private data using noise-injected embeddings, achieving a balance between privacy and utility.
LLMs can now perform inference without ever seeing raw text, opening the door to privacy-preserving applications without sacrificing performance.
Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substantial privacy risks, as unauthorized access may expose personal, medical, or legal information. Although prior defenses strived to mitigate these risks, they often incur substantial computational overhead and degrade model performance. To overcome this privacy-efficiency trade-off, we introduce Privacy-Preserving Fine-Tuning (PPFT), a novel training pipeline that eliminates the need for transmitting raw prompt text while maintaining a favorable balance between privacy preservation and model utility for both clients and service providers. Our approach operates in two stages: first, we train a client-side encoder together with a server-side projection module and LLM, enabling the server to condition on k-pooled prompt embeddings instead of raw text; second, we fine-tune the projection module and LLM on private, domain-specific data using noise-injected embeddings, allowing effective adaptation without exposing plain text prompts and requiring access to the decoder's internal parameters. Extensive experiments on domain-specific and general benchmarks demonstrate that PPFT achieves a striking balance between privacy and utility, maintaining competitive performance with minimal degradation compared to noise-free upper bounds.
