Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions

• URL: http://arxiv.org/abs/2408.05212v1
• Date: Sat, 10 Aug 2024 05:41:19 GMT
• Title: Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions
• Authors: Michele Miranda, Elena Sofia Ruzzetti, Andrea Santilli, Fabio Massimo Zanzotto, Sébastien Bratières, Emanuele Rodolà,
• Abstract summary: Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. Their reliance on massive internet-sourced datasets for training brings notable privacy issues. Certain application-specific scenarios may require fine-tuning these models on private data.
• Score: 12.451936012379319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy issues, which are exacerbated in critical domains (e.g., healthcare). Moreover, certain application-specific scenarios may require fine-tuning these models on private data. This survey critically examines the privacy threats associated with LLMs, emphasizing the potential for these models to memorize and inadvertently reveal sensitive information. We explore current threats by reviewing privacy attacks on LLMs and propose comprehensive solutions for integrating privacy mechanisms throughout the entire learning pipeline. These solutions range from anonymizing training datasets to implementing differential privacy during training or inference and machine unlearning after training. Our comprehensive review of existing literature highlights ongoing challenges, available tools, and future directions for preserving privacy in LLMs. This work aims to guide the development of more secure and trustworthy AI systems by providing a thorough understanding of privacy preservation methods and their effectiveness in mitigating risks.

Related papers

• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• Unique Security and Privacy Threats of Large Language Model: A Comprehensive Survey [46.19229410404056]
  Large language models (LLMs) have made remarkable advancements in natural language processing. These models are trained on vast datasets to exhibit powerful language understanding and generation capabilities. Privacy and security issues have been revealed throughout their life cycle.
  arXiv  Detail & Related papers  (2024-06-12T07:55:32Z)
• On Protecting the Data Privacy of Large Language Models (LLMs): A Survey [35.48984524483533]
  Large language models (LLMs) are complex artificial intelligence systems capable of understanding, generating and translating human language. LLMs process and generate large amounts of data, which may threaten data privacy.
  arXiv  Detail & Related papers  (2024-03-08T08:47:48Z)
• State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey [0.0]
  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)
• Privacy Preserving Large Language Models: ChatGPT Case Study Based Vision and Framework [6.828884629694705]
  This article proposes the conceptual model called PrivChatGPT, a privacy-generative model for LLMs. PrivChatGPT consists of two main components i.e., preserving user privacy during the data curation/pre-processing together with preserving private context and the private training process for large-scale data.
  arXiv  Detail & Related papers  (2023-10-19T06:55:13Z)
• 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)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Privacy in Deep Learning: A Survey [16.278779275923448]
  The ever-growing advances of deep learning in many areas have led to the adoption of Deep Neural Networks (DNNs) in production systems. The availability of large datasets and high computational power are the main contributors to these advances. This poses serious privacy concerns as this data can be misused or leaked through various vulnerabilities.
  arXiv  Detail & Related papers  (2020-04-25T23:47:25Z)

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.