Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation
Summary: arXiv:2604.06831v1
Type: Cross
Abstract: 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:
- Client-Side Encoder and Server-Side Projection: First, we train a client-side encoder together with a server-side projection module and Large Language Model (LLM), enabling the server to condition on k-pooled prompt embeddings instead of raw text.
- Fine-Tuning on Private Data: 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.
The Need for Privacy in Language Models
The increasing use of large language models in various applications has raised significant concerns regarding user privacy. Traditional methods require users to provide raw text inputs, which can inadvertently expose sensitive information. Such vulnerabilities have prompted researchers to explore alternatives that can safeguard user data while still leveraging the power of LLMs.
Introducing Privacy-Preserving Fine-Tuning (PPFT)
Privacy-Preserving Fine-Tuning (PPFT) represents a groundbreaking approach in this landscape. By eliminating the need for raw text submissions, PPFT addresses key privacy concerns. The dual-stage process not only enhances security but also ensures that the utility of the model is preserved, allowing clients and service providers to benefit from advanced AI capabilities without compromising personal information.
Key Advantages of PPFT
- Enhanced Privacy: Users can interact with LLMs without transmitting sensitive raw text, minimizing the risk of data breaches.
- Reduced Computational Overhead: By focusing on embeddings rather than extensive text, the computational demands on both the client and server sides are significantly lowered.
- High Utility: PPFT maintains competitive performance metrics, ensuring that users still receive high-quality responses from the model.
Implications for Future Research
The introduction of PPFT could pave the way for further advancements in privacy-preserving AI technologies. As the demand for secure AI solutions grows, the methodologies developed through this research may inspire other innovations aimed at enhancing user privacy across various AI applications.
In conclusion, the development of Privacy-Preserving Fine-Tuning marks an important step towards reconciling the need for privacy with the capabilities of large language models, setting a new standard for future AI deployments.