Summary of Power-softmax: Towards Secure Llm Inference Over Encrypted Data, by Itamar Zimerman et al.
Power-Softmax: Towards Secure LLM Inference over Encrypted Data
by Itamar Zimerman, Allon Adir, Ehud Aharoni, Matan Avitan, Moran Baruch, Nir Drucker, Jenny Lerner, Ramy Masalha, Reut Meiri, Omri Soceanu
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to implementing privacy-preserving Large Language Models (LLMs) using Homomorphic Encryption (HE). Specifically, it addresses the challenge of forming a polynomial representation of LLMs, which is necessary for modern cryptographic methods. The proposed method replaces non-polynomial components in Transformers with easier-to-approximate primitives before training, allowing for more efficient HE implementation. This approach could potentially introduce scalability challenges, but the authors suggest that their solution can mitigate these issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models private and secure using special math techniques called Homomorphic Encryption. Right now, it’s hard to make language models work with this technique because they need to be in a special mathematical form, like polynomials. The problem is that some parts of the model, like Softmax and layer normalization, aren’t polynomial shapes. Previous attempts tried to fix this by making big approximations or replacing tricky parts with simpler ones. But these approaches have their own problems, like being slow or not working well for large models. |
Keywords
* Artificial intelligence * Softmax
