用零知识证明让大模型在不看隐私数据的情况下给出个性化建议
Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs
- 结合零知识证明与大模型，验证用户特征而不暴露敏感信息
- 实验证明该方法在真实场景中可行，但存在性能限制
- 适合金融、医疗等需保护隐私的个性化服务场景
大型语言模型（LLMs）正被广泛应用于金融、医疗和人际关系等领域，为用户提供基于个人特质和上下文的定制化建议。然而，这种个性化通常依赖敏感数据，引发严重隐私问题，亟需数据最小化。为此，我们提出一种将零知识证明（ZKP）技术（特别是zkVM）与基于LLM的聊天机器人相结合的框架。该框架通过验证用户特质而无需披露敏感信息，实现隐私保护的数据共享。研究提出了相应的架构和提示策略，并通过实证评估明确了当前zkVM及所提提示策略在性能上的局限性，证实了其在真实场景中的可行性。
原文摘要 · Abstract (English)
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.
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