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Towards Encrypted Large Language Models with FHE

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Summary

This article discusses how Large Language Models (LLMs) have become increasingly popular tools for productivity, but come with the risk of leaking sensitive information to the service provider. To solve this, Zama proposes using Fully Homomorphic Encryption (FHE) to protect user privacy as well as the model owner's Intellectual Property (IP). The article explains how to use the Hugging Face transformers library to encrypt a single attention head of the multi-head attention (MHA) block and how to apply FHE to the GPT2 model. It also discusses the impact of quantization and complexity of FHE operations. Finally, it provides a link to the full code example and encourages readers to share their feedback.

Q&As

What is a Large Language Model (LLM) and how does it improve productivity?
Large Language Models (LLMs) are reliable tools for improving productivity in many areas such as programming, content creation, text analysis, web search, and distance learning.

What privacy concerns arise when using LLMs?
There is a risk of leaking sensitive information to the LLM service provider when using LLMs.

How can Fully Homomorphic Encryption (FHE) protect user privacy and model intellectual property when using LLMs?
Fully Homomorphic Encryption (FHE) enables the execution of functions on encrypted data, allowing the goal of protecting the model owner’s IP while still maintaining the privacy of the user's data.

How is the Hugging Face GPT2 model adapted to FHE?
The forward pass of modules that need to be encrypted is rewritten to include quantized operators. A SingleHeadQGPT2Model instance is built by first loading a GPT2LMHeadModel and then manually replacing the first multi-head attention module with a QGPT2SingleHeadAttention module. The forward pass is then overwritten so that the first head of the multi-head attention mechanism, including the projections made for building the query, keys and value matrices, is performed with FHE-friendly operators.

What are the advantages of using Concrete and Concrete-ML for ML model building and conversion to FHE?
Concrete and Concrete-ML allow straightforward ML model building and conversion to the FHE equivalent, allowing users to compute and predict over encrypted data while preserving user privacy and model intellectual property.

AI Comments

👍 This article is a great example of how to use FHE to protect user privacy while still being able to leverage the power of LLMs.

👎 This article is too technical and could be better explained for readers who are not as familiar with FHE.

AI Discussion

Me: It's about how Fully Homomorphic Encryption (FHE) can be used to solve the privacy challenges of Large Language Models (LLMs). They discuss the implications of the user's queries being processed by the models and the risk of revealing sensitive information to the LLM service provider.

Friend: That's really interesting. What other implications does the article discuss?

Me: The article also talks about how FHE can protect both the privacy of the user and the Intellectual Property (IP) of the model. It discusses how to adapt the GPT2 implementation from the Hugging Face transformers library and how to use quantization and PBS operations to express any sub-part or even the full LLM computation in FHE. It also covers the impact of quantization on the accuracy of the model and how to implement a single attention head with FHE. Finally, it talks about the complexity of the computations and the potential of hardware improvements in the future.

Action items

Explore the Hugging Face transformers library and the Concrete-Python library to understand how to convert Python functions into their FHE equivalents.
Experiment with post-training quantization to convert model weights and activations to integers.
Implement the FHE compatible attention mechanism and examine the impact on LLM accuracy.

Technical terms

Large Language Models (LLM): A type of artificial intelligence model that uses natural language processing to understand and generate text.
Fully Homomorphic Encryption (FHE): A type of encryption that allows computations to be performed on encrypted data without decrypting it.
Hugging Face transformers library: A library of pre-trained language models developed by Hugging Face.
Concrete-Python: A library for converting Python functions into their FHE equivalents.
Programmable Bootstrapping (PBS): An operation that implements a table lookup (TLU) operation on encrypted data while also refreshing ciphertexts to allow arbitrary computation.
Quantization: The process of converting a model's weights and activations to integers.
Post-training quantization: A type of quantization that does not require re-training the model.
Multi-head Attention (MHA): A type of attention mechanism used in transformer models.
TFHE: A library for performing Fully Homomorphic Encryption.