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Illustration of a cybernetic forest. Tree-like structures symbolize Large Language Models (LLMs), with their roots diving deep into a digital ground. Sinister digital creatures, representing privacy risks, lurk in the shadows, attempting to corrupt the data flow. Luminous guardians, embodying defenses, ward off these threats, while AI-powered owls oversee, denoting future security services ensuring the protection of the forest.

Privacy in Large Language Models: Attacks, Defenses and Future Directions

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WorkDifferentWithAI.com Academic Paper Alert!

Written by Haoran Li, Yulin Chen, Jinglong Luo, Yan Kang, Xiaojin Zhang, Qi Hu, Chunkit Chan, Yangqiu Song

Category: “AI for IT”

Article Section: Ethical and Responsible AI; AI Governance Frameworks

Publication Date: 2023-10-16

SEO Description: “Exploring privacy risks and defenses in Large Language Models (LLMs) for future AI security.”

Li, Haoran, et al. Privacy in Large Language Models: Attacks, Defenses and Future Directions. arXiv:2310.10383, arXiv, 16 Oct. 2023, http://arxiv.org/abs/2310.10383.

AI-Generated Paper Summary

GPT-4 API

The academic paper “Privacy in Large Language Models: Attacks, Defenses and Future Directions” discusses the privacy implications surrounding the use and progression of large language models (LLMs). Authored by Haoran Li and seven other contributors, the paper explores privacy concerns, including potential risks from unrestricted access to these models. It presents a comprehensive analysis of privacy attacks on LLMs and bottlenecks, categorizing them based on an assumed adversary’s capabilities. The authors also review prominent defense strategies formulated to mitigate these privacy attacks. The paper not only gives an overview of current privacy issues in LLMs but also anticipates possible future concerns and directions for exploration.

Claud.ai Full PDF Analysis

Based on my analysis, here are some key points about the novelty and potential commercial applications of this paper:

Novelty:

  • Provides a comprehensive survey of the latest privacy attacks and defenses for large language models (LLMs). Incorporates very recent works from 2022-2023.
  • Systematically categorizes privacy attacks based on the adversary’s assumed capabilities (black-box, white-box, access to embeddings/gradients, etc). This provides a clear framework to understand different threats.
  • Discusses emerging attacks like prompt injection and jailbreaking that exploit LLMs’ instruction following abilities. These are important new attack vectors as LLMs become more capable.
  • Analyzes limitations of existing attacks – many make unrealistic assumptions about adversary capabilities. Calls for more practical motivations and evaluations.
  • Identifies under-explored threats like prompt extraction attacks and side channel attacks. Useful for directing future research.

Commercial Applications:

  • Surveys differential privacy, federated learning, and secure multi-party computation defenses. Could inform development of commercial LLM services with privacy protections.
  • Discussion of limitations of differential privacy provides insights into improving utility of private LLMs. Important for balancing privacy vs performance.
  • Analysis of secure multi-party computation strategies highlights tradeoffs between efficiency vs model versatility. Relevant for private LLM APIs.
  • Federated learning analysis suggests techniques like freezing word embeddings during training to prevent gradient leakage. Useful for collaborative LLM training.
  • Reinforcement learning from human feedback identified as a strategy to improve LLM privacy and safety. Valuable for commercial chatbots and assistants.

Overall, the paper provides a comprehensive analysis of an important emerging problem – privacy in LLMs. The categorization of attacks and limitations identified suggest promising directions for future research and defensive techniques relevant to developing private and ethical commercial LLMs.

Keywords

Privacy, Large Language Models, Attacks, Defenses, Future Directions

Author’s Abstract

The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines. On the one hand, powerful language models, trained on massive textual data, have brought unparalleled accessibility and usability for both models and users. On the other hand, unrestricted access to these models can also introduce potential malicious and unintentional privacy risks. Despite ongoing efforts to address the safety and privacy concerns associated with LLMs, the problem remains unresolved. In this paper, we provide a comprehensive analysis of the current privacy attacks targeting LLMs and categorize them according to the adversary’s assumed capabilities to shed light on the potential vulnerabilities present in LLMs. Then, we present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks. Beyond existing works, we identify upcoming privacy concerns as LLMs evolve. Lastly, we point out several potential avenues for future exploration.

Read the full paper here

Last updated on October 22nd, 2023.