[2603.01499] Towards Privacy-Preserving LLM Inference via Collaborative Obfuscation (Technical Report)

Computer Science > Cryptography and Security arXiv:2603.01499 (cs) [Submitted on 2 Mar 2026] Title:Towards Privacy-Preserving LLM Inference via Collaborative Obfuscation (Technical Report) Authors:Yu Lin, Qizhi Zhang, Wenqiang Ruan, Daode Zhang, Jue Hong, Ye Wu, Hanning Xia, Yunlong Mao, Sheng Zhong View a PDF of the paper titled Towards Privacy-Preserving LLM Inference via Collaborative Obfuscation (Technical Report), by Yu Lin and 8 other authors View PDF HTML (experimental) Abstract:The rapid development of large language models (LLMs) has driven the widespread adoption of cloud-based LLM inference services, while also bringing prominent privacy risks associated with the transmission and processing of private data in remote inference. For privacy-preserving LLM inference technologies to be practically applied in industrial scenarios, three core requirements must be satisfied simultaneously: (1) Accuracy and efficiency losses should be minimized to mitigate degradation in service experience. (2) The inference process can be run on large-scale clusters consist of heterogeneous legacy xPUs. (3) Compatibility with existing LLM infrastructures should be ensured to reuse their engineering optimizations. To the best of our knowledge, none of the existing privacy-preserving LLM inference methods satisfy all the above constraints while delivering meaningful privacy guarantees. In this paper, we propose AloePri, the first privacy-preserving LLM inference method for industrial app...