Large language models (LLMs) like ChatGPT are revolutionizing how we interact with technology, but what about the privacy of our conversations? This new research explores the potential risks of using online LLM services (AIaaS) and proposes a clever framework to keep your interactions private. Currently, when you use an online LLM, the service provider can potentially track your requests, analyze your patterns, and even link your queries back to you. This raises some serious privacy concerns, especially if you're dealing with sensitive information. This research introduces a practical solution: a privacy-preserving framework that uses a cryptographic technique called 'partially blind signatures.' Imagine sending your request in a sealed envelope that the LLM can process without ever opening. The LLM can still understand and respond to your request, but it can't see the content itself, nor can it link it back to you. This framework is designed to be compatible with existing LLM services like ChatGPT without requiring significant changes to their infrastructure. It works with both subscription and pay-per-use models, adapting to different billing structures while maintaining user anonymity. The researchers have tested their approach and found that the added privacy comes with minimal impact on performance. This means you can enjoy the power of LLMs without sacrificing your privacy. The future of LLMs depends on trust, and frameworks like this one pave the way for a more secure and private AI-powered world. As LLMs continue to evolve, protecting user data becomes even more critical, and this research provides a practical step in that direction.