How LLMs Can Give Personalized Advice Without Seeing Your Private Data

What is it about?

Many chatbots and online advisors now use powerful large language models (LLMs) to give advice that fits each person’s needs. However, to do this, they often ask for private information, like your income or medical history. Sharing such personal details raises concerns about privacy and data security. In this research, we introduce a new way for LLM-based chatbots to provide helpful, personalized advice without ever seeing or storing your private data. We use a cryptography method called “zero-knowledge proof,” which allows users to prove that certain traits or categories apply to them (for example, that they fall into a specific risk group or health category), without revealing any sensitive details. This approach aims to combine the benefits of LLM-powered advice with strong privacy protection.

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Photo by Jason Dent on Unsplash

Photo by Jason Dent on Unsplash

Why is it important?

Our work is among the first to combine zero-knowledge proof technology with large language model chatbots to protect user privacy while still allowing personalized advice. Unlike previous systems, our approach does not require users to share their sensitive information directly with the LLM. We also demonstrate that our method can work in practical, real-world settings, such as financial and health advice, and we show that proof generation and verification can be done efficiently. This new approach could help make LLM-powered services safer and more widely accepted, especially as privacy regulations become stricter.

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