ProxyGPT: Enabling Anonymous Queries in AI Chatbots with (Un)Trustworthy Browser Proxies
<|reference_start|>ProxyGPT: Enabling Anonymous Queries in AI Chatbots with (Un)Trustworthy Browser Proxies: AI-powered chatbots (ChatGPT, Claude, etc.) require users to create an account using their email and phone number, thereby linking their personally identifiable information to their conversational data and usage patterns. As these chatbots are increasingly being used for tasks involving sensitive information, privacy concerns have been raised about how chatbot providers handle user data. To address these concerns, we present ProxyGPT, a privacy-enhancing system that enables anonymous queries in popular chatbot platforms. ProxyGPT leverages volunteer proxies to submit user queries on their behalf, thus providing network-level anonymity for chatbot users. The system is designed to support key security properties such as content integrity via TLS-backed data provenance, end-to-end encryption, and anonymous payment, while also ensuring usability and sustainability. We provide a thorough analysis of the privacy, security, and integrity of our system and identify various future research directions, particularly in the area of private chatbot query synthesis. Our human evaluation shows that ProxyGPT offers users a greater sense of privacy compared to traditional AI chatbots, especially in scenarios where users are hesitant to share their identity with chatbot providers. Although our proof-of-concept has higher latency than popular chatbots, our human interview participants consider this to be an acceptable trade-off for anonymity. To the best of our knowledge, ProxyGPT is the first comprehensive proxy-based solution for privacy-preserving AI chatbots. Our codebase is available at https://github.com/dzungvpham/proxygpt.<|reference_end|>
@article{pham2024proxygpt:,
    title={ProxyGPT: Enabling Anonymous Queries in AI Chatbots with (Un)Trustworthy
  Browser Proxies},
    author={Dzung Pham, Jade Sheffey, Chau Minh Pham, Amir Houmansadr},
    journal={arXiv preprint arXiv:2407.08792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08792},
    primaryClass={cs.CR}
}