Summary of Position: On-premises Llm Deployment Demands a Middle Path: Preserving Privacy Without Sacrificing Model Confidentiality, by Hanbo Huang et al.
Position: On-Premises LLM Deployment Demands a Middle Path: Preserving Privacy Without Sacrificing Model Confidentiality
by Hanbo Huang, Yihan Li, Bowen Jiang, Lin Liu, Bo Jiang, Ruoyu Sun, Zhuotao Liu, Shiyu Liang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents a novel approach to deploying large language models (LLMs) within user-controlled infrastructure, enhancing data privacy and mitigating misuse risks. The authors argue that closed-source LLMs can be deployed on-premises, offering better security than open-weight models while ensuring model confidentiality by preventing theft. They introduce a semi-open deployment framework that secures only a few carefully chosen layers, achieving distillation resistance comparable to fully secured models while preserving fine-tuning flexibility. Experimental results show that securing bottom layers significantly reduces functional extraction risks, demonstrating that privacy and confidentiality can coexist. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make big language models safer to use without giving away too much information. Right now, we have two ways to customize these models: one that requires sending private data to outside servers, or another that allows local fine-tuning but could be misused. The authors think that putting the model on your own server is a better way to go because it keeps your data safer and reduces the risk of misuse. They came up with a new way to do this called semi-open deployment, which only locks down a few important parts of the model. This makes it harder for someone to steal or use the model in a bad way, while still letting you customize it as needed. |
Keywords
» Artificial intelligence » Distillation » Fine tuning
