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Towards Privacy-Preserving LLM Inference via Collaborative Obfuscation (Technical Report)
arXiv – CS AI|Yu Lin, Qizhi Zhang, Wenqiang Ruan, Daode Zhang, Jue Hong, Ye Wu, Hanning Xia, Yunlong Mao, Sheng Zhong||2 views
🤖AI Summary
Researchers have developed AloePri, the first privacy-preserving LLM inference method designed for industrial applications. The system uses collaborative obfuscation to protect input/output data while maintaining 96.5-100% accuracy and resisting state-of-the-art attacks, successfully tested on a 671B parameter model.
Key Takeaways
- →AloePri is the first privacy-preserving LLM inference method that meets all three industrial requirements: minimal accuracy loss, scalability on heterogeneous hardware, and infrastructure compatibility.
- →The system achieved 0.0-3.5% accuracy loss while maintaining efficiency equivalent to plaintext inference on the 671B parameter Deepseek-V3.1-Terminus model.
- →AloePri successfully resisted state-of-the-art attacks with less than 5% of tokens being recovered during testing.
- →The method uses covariant obfuscation to jointly transform both data and model parameters for enhanced privacy protection.
- →This represents the first practical privacy-preserving solution for large-scale LLM deployments in real-world industrial systems.
#privacy-preserving#llm-inference#industrial-ai#data-protection#collaborative-obfuscation#enterprise-ai#ai-security#cloud-computing
Read Original →via arXiv – CS AI
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