Unlinkable Inference as a User Privacy Architecture (openanonymity.ai)

A new privacy technique, "unlinkable inference," has been proposed to enhance user privacy when interacting with AI models. This method ensures that each AI request is sandboxed from one another and detached from a user's identity, countering the trend of data linkage that currently enables AI providers to build detailed user profiles based on shared prompts. As the use of AI continues to grow, particularly through chat interfaces like ChatGPT, the potential for privacy breaches increases significantly, making this development crucial for safeguarding sensitive user information. The unlinkable inference model employs two main techniques: blind signatures and secure inference proxies. Blind signatures allow users to authenticate their requests without revealing their identity, while secure proxies facilitate this anonymity by handling user requests without accessing the content. This architecture not only protects individual privacy but also minimizes the risk of data leakage, addressing a pressing issue in the AI/ML domain. The implementation of unlinkable inference, tested in a private alpha AI chat application, demonstrates its practical viability and highlights its importance for advancing privacy standards within the rapidly evolving landscape of AI technologies.