PrivateChat: A Secure Encrypted Communication Framework with Black-box LLMs

Jiayi Yuan, Li Xu, Haobo Jiang, Hossein Rahmani, De Wen Soh, Jun Liu

Keywords: cloud LLM privacy protection, encrypted communication, black-box optimization

Abstract: With the growing applications of large language models (LLMs), privacy leakage has emerged as a significant concern. However, widely used LLMs are often deployed on cloud platforms and accessible only through relatively expensive API calls, complicating the realization of secure communication between users and cloud LLMs. In this paper, we introduce PrivateChat, a novel private communication framework that enables users to safely interact with cloud LLMs using user-customized encryption methods (e.g., AES). Our core idea is to learn a private system prompt, which instructs the cloud LLM to process and respond in encrypted text while concealing encryption details from potential attackers. Additionally, to optimize such prompts with few API calls, we propose a Sample-Efficient Simultaneous Perturbation Stochastic Approximation (SE-SPSA) black-box optimization algorithm, which incorporates a baseline-based variance reduction strategy with SPSA for effective and economical training. Extensive experiments on several benchmark datasets with various encryption methods show the effectiveness of our approach in achieving secure and reliable communication with cloud LLMs.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 13178