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

• URL: http://arxiv.org/abs/2502.06425v1
• Date: Mon, 10 Feb 2025 13:02:00 GMT
• Title: Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs
• Authors: Hiroki Watanabe, Motonobu Uchikoshi,
• Abstract summary: We propose a framework that integrates zero-knowledge proof technology, specifically zkVM, with large language models (LLMs) This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information.
• Score: 0.6906005491572401
• License:
• Abstract: 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.

Related papers

• Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions [11.338466798715906]
  Fine-tuning Large Language Models (LLMs) can achieve state-of-the-art performance across various domains. This paper provides a comprehensive survey of privacy challenges associated with fine-tuning LLMs. We highlight vulnerabilities to various privacy attacks, including membership inference, data extraction, and backdoor attacks.
  arXiv  Detail & Related papers  (2024-12-21T06:41:29Z)
• Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions [0.0]
  This paper introduces the Privacy-Preserving Zero-Shot Learning (PP-ZSL) framework, a novel approach leveraging large language models (LLMs) in a zero-shot learning mode. Unlike conventional machine learning methods, PP-ZSL eliminates the need for local training on sensitive data by utilizing pre-trained LLMs to generate responses directly. The framework incorporates real-time data anonymization to redact or mask sensitive information, retrieval-augmented generation (RAG) for domain-specific query resolution, and robust post-processing to ensure compliance with regulatory standards.
  arXiv  Detail & Related papers  (2024-12-10T17:20:47Z)
• Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions [0.0]
  This survey explores the landscape of privacy-preserving mechanisms tailored for large language models. We examine their efficacy in addressing key privacy challenges, such as membership inference and model inversion attacks. By synthesizing state-of-the-art approaches and future trends, this paper provides a foundation for developing robust, privacy-preserving large language models.
  arXiv  Detail & Related papers  (2024-12-09T00:24:09Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Text anonymization is crucial for sharing sensitive data while maintaining privacy. Existing techniques face the emerging challenges of re-identification attack ability of Large Language Models. This paper proposes a framework composed of three LLM-based components -- a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
  Security measures such as anonymization are needed to protect individuals' privacy. Within our study, we conducted expert interviews to gain insights into practices in the field. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
  arXiv  Detail & Related papers  (2024-07-04T08:29:27Z)
• LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework synergizes open-world knowledge with collaborative knowledge. We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• Synergizing Privacy and Utility in Data Analytics Through Advanced Information Theorization [2.28438857884398]
  We introduce three sophisticated algorithms: a Noise-Infusion Technique tailored for high-dimensional image data, a Variational Autoencoder (VAE) for robust feature extraction and an Expectation Maximization (EM) approach optimized for structured data privacy. Our methods significantly reduce mutual information between sensitive attributes and transformed data, thereby enhancing privacy. The research contributes to the field by providing a flexible and effective strategy for deploying privacy-preserving algorithms across various data types.
  arXiv  Detail & Related papers  (2024-04-24T22:58:42Z)
• State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey [0.9208007322096533]
  This paper examines the evolving landscape of machine learning (ML) and its profound impact across various sectors. It focuses on the emerging field of Privacy-preserving Machine Learning (PPML) As ML applications become increasingly integral to industries like telecommunications, financial technology, and surveillance, they raise significant privacy concerns.
  arXiv  Detail & Related papers  (2024-02-25T17:31:06Z)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients [54.98496284653234]
  We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions. We solve this problem by introducing a regularizer based on the mutual information between the sensitive state and the actions. We develop a model-based estimator for optimization of privacy-constrained policies.
  arXiv  Detail & Related papers  (2020-12-30T03:22:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.