A Practical and Privacy-Preserving Framework for Real-World Large Language Model Services

• URL: http://arxiv.org/abs/2411.01471v1
• Date: Sun, 03 Nov 2024 07:40:28 GMT
• Title: A Practical and Privacy-Preserving Framework for Real-World Large Language Model Services
• Authors: Yu Mao, Xueping Liao, Wei Liu, Anjia Yang,
• Abstract summary: Large language models (LLMs) have demonstrated exceptional capabilities in text understanding and generation. Individuals often rely on online AI as a Service (AI) provided by LLM companies. This business model poses significant privacy risks, as service providers may exploit users' trace patterns and behavioral data. We propose a practical and privacy-preserving framework that ensures user anonymity by preventing service providers from linking requests to the individuals who submit them.
• Score: 8.309281698695381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated exceptional capabilities in text understanding and generation, and they are increasingly being utilized across various domains to enhance productivity. However, due to the high costs of training and maintaining these models, coupled with the fact that some LLMs are proprietary, individuals often rely on online AI as a Service (AIaaS) provided by LLM companies. This business model poses significant privacy risks, as service providers may exploit users' trace patterns and behavioral data. In this paper, we propose a practical and privacy-preserving framework that ensures user anonymity by preventing service providers from linking requests to the individuals who submit them. Our framework is built on partially blind signatures, which guarantee the unlinkability of user requests. Furthermore, we introduce two strategies tailored to both subscription-based and API-based service models, ensuring the protection of both users' privacy and service providers' interests. The framework is designed to integrate seamlessly with existing LLM systems, as it does not require modifications to the underlying architectures. Experimental results demonstrate that our framework incurs minimal computation and communication overhead, making it a feasible solution for real-world applications.

Related papers

• Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
  We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG) FedE4RAG facilitates collaborative training of client-side RAG retrieval models. We apply homomorphic encryption within federated learning to safeguard model parameters.
  arXiv  Detail & Related papers  (2025-04-27T04:26:02Z)
• A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation [0.6699777383856287]
  ChatGPT services leverage cloud-based large language models (LLMs) Privacy concerns arise as prompts are transmitted and processed by the model providers. We propose a general pseudonymization framework applicable to cloud-based LLMs.
  arXiv  Detail & Related papers  (2025-02-21T06:15:53Z)
• Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs [0.6906005491572401]
  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.
  arXiv  Detail & Related papers  (2025-02-10T13:02:00Z)
• Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions [59.5243730853157]
  Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets. This article conducts a comparative analysis of three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues.
  arXiv  Detail & Related papers  (2025-01-08T11:37:06Z)
• 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)
• FedSpaLLM: Federated Pruning of Large Language Models [8.45879077052023]
  Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. We propose FedSpaLLM, the first federated learning framework designed specifically for pruning LLMs.
  arXiv  Detail & Related papers  (2024-10-18T20:33:12Z)
• KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server [48.04903443425111]
  Large language models (LLMs) facilitate many parties to fine-tune LLMs on their own private data. Existing solutions, such as utilizing synthetic data for substitution, struggle to simultaneously improve performance and preserve privacy. We propose KnowledgeSG, a novel client-server framework which enhances synthetic data quality and improves model performance while ensuring privacy.
  arXiv  Detail & Related papers  (2024-10-08T06:42:28Z)
• Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• 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)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models [29.58928014528991]
  PDSS works on a server-client architecture, wherein client transmits prompts to the server's LLM for rationale generation. The generated rationales are then decoded by the client and used to enrich the training of task-specific small language model. Experiments demonstrate the effectiveness of PDSS in various text generation tasks, enabling the training of task-specific SLM with enhanced performance.
  arXiv  Detail & Related papers  (2024-06-18T08:48:14Z)
• PFID: Privacy First Inference Delegation Framework for LLMs [34.59282305562392]
  This paper introduces a novel privacy-preservation framework named PFID for LLMs. It addresses critical privacy concerns by localizing user data through model sharding and singular value decomposition.
  arXiv  Detail & Related papers  (2024-06-18T03:27:09Z)
• Privacy and Security Implications of Cloud-Based AI Services : A Survey [4.1964397179107085]
  This paper details the privacy and security landscape in today's cloud ecosystem. It identifies that there is a gap in addressing the risks introduced by machine learning models.
  arXiv  Detail & Related papers  (2024-01-31T13:30:20Z)
• Privacy in Large Language Models: Attacks, Defenses and Future Directions [84.73301039987128]
  We analyze the current privacy attacks targeting large language models (LLMs) and categorize them according to the adversary's assumed capabilities. We present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks.
  arXiv  Detail & Related papers  (2023-10-16T13:23:54Z)
• 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)
• Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection [6.201275002179716]
  We introduce the HaS framework, where "H(ide)" and "S(eek)" represent its two core processes: hiding private entities for anonymization and seeking private entities for de-anonymization. To quantitatively assess HaS's privacy protection performance, we propose both black-box and white-box adversarial models.
  arXiv  Detail & Related papers  (2023-09-06T14:54:11Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)

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.