# Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs

arXiv: https://arxiv.org/abs/2502.06425 · HTML: https://mlsanity.com/paper/2502.06425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [zkLLM: Zero Knowledge Proofs for Large Language Models](https://mlsanity.com/paper/2404.16109/) (2024)
- [Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination](https://mlsanity.com/paper/2312.13581/) (2023)

## Abstract (arXiv)

Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.