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The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks

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WorkDifferentWithAI.com Academic Paper Alert!

Written by Xiaoyi Chen, Siyuan Tang, Rui Zhu, Shijun Yan, Lei Jin, Zihao Wang, Liya Su, XiaoFeng Wang, Haixu Tang

Category: “AI for IT”

Article Section: Ethical and Responsible AI; Responsible AI Practices

Publication Date: 2023-10-23

SEO Description: “Exploring privacy risks in AI: Janus attack in Language Models reveals potential PII leaks during fine-tuning process.”

Chen, Xiaoyi, et al. The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks. arXiv:2310.15469, arXiv, 23 Oct. 2023, http://arxiv.org/abs/2310.15469.

AI-Generated Paper Summary

GPT-4 API

The research paper titled “The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks” underscores a potential privacy risk posed by fine-tuning Large Language Models (LLMs) such as OpenAI’s GPT-3.5. In the era post-2018, advancements in LLMs have led to immense linguistic capabilities, but also unintentional appropriation of Personal Identifiable Information (PII) during web-based data retrieval. The Janus attack, a new LLM exploitation method discovered in this study, demonstrates that fine-tuning LLMs using a small PII dataset might resurrect and reveal hidden PIIs. This implies that despite the application of counter measures like RLHF during training and Catastrophic Forgetting, the utility of these models can still lead to significant privacy infringements. The paper calls for an intricate balance between LLM utility and privacy maintenance.

Claude.ai Full PDF Analysis

Here is my analysis of the key points and implications from the paper:

Novelty:

  • The paper introduces a new attack method called “Janus” that can extract private information from large language models (LLMs) through fine-tuning. This is a novel attack vector not explored in prior work.
  • Janus allows an attacker to recover forgotten personal identifiable information (PII) that was originally part of the LLM’s training data by fine-tuning the model on a small set of similar PII data. This highlights the privacy risks of fine-tuning interfaces for LLMs.
  • The paper provides new analysis and insights into how fine-tuning helps revive forgotten tasks in LLMs, enabling the recovery of private data. This includes using tools like Centered Kernel Alignment to analyze the latent spaces.
  • The study reveals counter-intuitive findings compared to prior work on deep learning models, where fine-tuning typically makes extraction of training data harder. The reasons behind this difference for LLMs are analyzed.

Commercial Applications:

  • The findings spotlight the need for enhanced privacy-preserving strategies when developing commercial applications involving fine-tuning of large language models.
  • Companies offering LLM APIs with fine-tuning capabilities should implement mechanisms to scrutinize fine-tuning data to detect potential privacy violations.
  • The results can inform the development of techniques to inject noise during LLM training to make subsequent fine-tuning based privacy attacks harder.
  • The study suggests prompts alone may not be sufficient for extracting private data from LLMs due to catastrophic forgetting. Companies should be aware of risks from fine-tuning.
  • Understanding privacy vulnerabilities and extraction techniques for LLMs is useful for commercial providers to improve security and prevent data abuse.

In summary, the paper presents novel analysis of privacy risks from LLM fine-tuning and provides useful insights for developing more secure commercial LLM applications. The findings highlight the need for safeguards when enabling fine-tuning capabilities.

Keywords

Janus Interface, Large Language Models, Fine-Tuning, Privacy Risks, Personal Identifiable Information

Author’s Abstract

The era post-2018 marked the advent of Large Language Models (LLMs), with innovations such as OpenAI’s ChatGPT showcasing prodigious linguistic prowess. As the industry galloped toward augmenting model parameters and capitalizing on vast swaths of human language data, security and privacy challenges also emerged. Foremost among these is the potential inadvertent accrual of Personal Identifiable Information (PII) during web-based data acquisition, posing risks of unintended PII disclosure. While strategies like RLHF during training and Catastrophic Forgetting have been marshaled to control the risk of privacy infringements, recent advancements in LLMs, epitomized by OpenAI’s fine-tuning interface for GPT-3.5, have reignited concerns. One may ask: can the fine-tuning of LLMs precipitate the leakage of personal information embedded within training datasets? This paper reports the first endeavor to seek the answer to the question, particularly our discovery of a new LLM exploitation avenue, called the Janus attack. In the attack, one can construct a PII association task, whereby an LLM is fine-tuned using a minuscule PII dataset, to potentially reinstate and reveal concealed PIIs. Our findings indicate that, with a trivial fine-tuning outlay, LLMs such as GPT-3.5 can transition from being impermeable to PII extraction to a state where they divulge a substantial proportion of concealed PII. This research, through its deep dive into the Janus attack vector, underscores the imperative of navigating the intricate interplay between LLM utility and privacy preservation.

Read the full paper here

Last updated on November 5th, 2023.