LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance

Large language models (LLMs), such as Microsoft's Copilot, OpenAI's GPT, and Google's Gemini, have substantially advanced a wide range of applications, including text summarization, content generation, and software development. Despite their impressive capabilities, these LLM services pose significant privacy risks. User prompts, which often contain sensitive personal or organizational information, are transmitted to third-party servers, where they may be vulnerable to data breaches, unauthorized access, or inference attacks. Recent work by Chu et al. [1] demonstrated that adversaries could exploit GPT models to extract private information through carefully crafted prompts, emphasizing the critical importance of privacy protection in LLM interactions. Various privacy-preserving techniques have been developed for LLMs, including cryptography-based and perturbation-based methods.