AI BriefingFHE-Based LLM Privacy Protection Technology
Key point
This introduces an LLM inference technology that uses Fully Homomorphic Encryption (FHE) to simultaneously protect user data and model IP.
Details
To address the problems of user data leakage and protecting model intellectual property (IP) that arise when using LLMs, this presents an approach that applies Fully Homomorphic Encryption (FHE) technology.
Zama explains a method for performing some LLM operations on encrypted data using Hugging Face's transformers library and Concrete-Python.
The key technical features are as follows:
- Data and Model Protection: The user's query is encrypted before being sent to the server, and the server performs operations on the encrypted data, minimizing the risk of data leakage. At the same time, the model weights remain securely on the server, protecting the IP.
- Computation Method: Using TFHE, model weights and activation functions are represented as integers, and nonlinear functions are handled through Programmable Bootstrapping (PBS) operations.
- Implementation Example: Some of the Multi-Head Attention (MHA) layers of the GPT-2 model are implemented with FHE, demonstrating a hybrid inference approach between client and server.
For successful implementation, a Quantization process that converts model weights and activation values into integers is essential, which helps maintain the model's prediction quality even on encrypted values.
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