Large Language Models (LLMs) have revolutionized the field of artificial intelligence, with ChatGPT leading the way. However, concerns around privacy and security have arisen due to the power and capabilities of these models. To address these concerns, a framework called Puma has been developed.
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PUMA merges secure multi-party computation (MPC) with efficient Transformer inference to ensure the privacy and security of data during evaluation. It introduces a novel technique to approximate complex non-linear functions within Transformer Models, optimizing efficiency while maintaining accuracy.
The secure inference process in PUMA involves three entities: the model owner, the client, and the computing parties. The model owner provides the trained Transformer models, the client contributes the input data, and the computing parties execute secure computation protocols to protect the data and model weights.
PUMA’s seamless integration makes it easy to use with existing Transformer models, requiring minimal modifications. It offers a secure embedding design that aligns closely with the standard workflow of Transformer models, simplifying the deployment of secure models in practical applications.
Additionally, PUMA addresses challenges in approximating complex functions like GeLU and Softmax with accurate approximations tailored to their properties. It also tackles the computation of LayerNorm with secure protocols, ensuring both security and efficiency.
By utilizing PUMA, developers can harness the power of LLMs without compromising privacy and security. The framework provides a secure and efficient solution for evaluating Transformer models, safeguarding sensitive data while delivering accurate results.
Sources:
– Paper: [PUMA: Privacy-Preserving Multi-Party Computation Framework for Efficient Evaluation of Transformer Models](https://arxiv.org/abs/2207.10513)
– GitHub: [PUMA: Privacy-Preserving Multi-Party Computation Framework](https://github.com/OpenMined/PUMA)
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