A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
- URL: http://arxiv.org/abs/2309.08230v2
- Date: Mon, 15 Jan 2024 06:59:29 GMT
- Title: A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
- Authors: Hongsheng Hu, Shuo Wang, Jiamin Chang, Haonan Zhong, Ruoxi Sun, Shuang Hao, Haojin Zhu, Minhui Xue,
- Abstract summary: We try to explore the potential threats posed by unlearning services in Machine Learning (ML)
We propose two strategies that leverage over-unlearning to measure the impact on the trade-off balancing.
Results indicate significant potential for both strategies to undermine model efficacy in unlearning scenarios.
- Score: 31.347825826778276
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retraining a model from scratch to fulfill the unlearning request is impractical due to the lack of training data on the service provider's side (the server). Furthermore, approximate unlearning further embraces a complex trade-off between utility (model performance) and privacy (unlearning performance). In this paper, we try to explore the potential threats posed by unlearning services in MLaaS, specifically over-unlearning, where more information is unlearned than expected. We propose two strategies that leverage over-unlearning to measure the impact on the trade-off balancing, under black-box access settings, in which the existing machine unlearning attacks are not applicable. The effectiveness of these strategies is evaluated through extensive experiments on benchmark datasets, across various model architectures and representative unlearning approaches. Results indicate significant potential for both strategies to undermine model efficacy in unlearning scenarios. This study uncovers an underexplored gap between unlearning and contemporary MLaaS, highlighting the need for careful considerations in balancing data unlearning, model utility, and security.
Related papers
- Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning [7.557226714828334]
We present a novel unlearning mechanism designed to remove the impact of specific data samples from a neural network.
In achieving this goal, we crafted a novel loss function tailored to eliminate privacy-sensitive information from weights and activation values of the target model.
Our results showcase the superior performance of our approach in terms of unlearning efficacy and latency as well as the fidelity of the primary task.
arXiv Detail & Related papers (2024-07-01T00:20:26Z) - UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI [50.61495097098296]
We revisit the paradigm in which unlearning is used for Large Language Models (LLMs)
We introduce a concept of ununlearning, where unlearned knowledge gets reintroduced in-context.
We argue that content filtering for impermissible knowledge will be required and even exact unlearning schemes are not enough for effective content regulation.
arXiv Detail & Related papers (2024-06-27T10:24:35Z) - Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning [97.2995389188179]
Recent research has begun to approach large language models (LLMs) unlearning via gradient ascent (GA)
Despite their simplicity and efficiency, we suggest that GA-based methods face the propensity towards excessive unlearning.
We propose several controlling methods that can regulate the extent of excessive unlearning.
arXiv Detail & Related papers (2024-06-13T14:41:00Z) - Adversarial Machine Unlearning [26.809123658470693]
This paper focuses on the challenge of machine unlearning, aiming to remove the influence of specific training data on machine learning models.
Traditionally, the development of unlearning algorithms runs parallel with that of membership inference attacks (MIA), a type of privacy threat.
We propose a game-theoretic framework that integrates MIAs into the design of unlearning algorithms.
arXiv Detail & Related papers (2024-06-11T20:07:22Z) - Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning [16.809644622465086]
We conduct the first investigation to understand the extent to which machine unlearning can leak the confidential content of unlearned data.
Under the Machine Learning as a Service setting, we propose unlearning inversion attacks that can reveal the feature and label information of an unlearned sample.
The experimental results indicate that the proposed attack can reveal the sensitive information of the unlearned data.
arXiv Detail & Related papers (2024-04-04T06:37:46Z) - The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
Large Language Models (LLMs) are foundational to AI advancements.
LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information.
Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
arXiv Detail & Related papers (2024-03-23T09:26:15Z) - Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
We explore machine unlearning in the domain of large language models (LLMs)
This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
arXiv Detail & Related papers (2024-02-13T20:51:58Z) - Unlearnable Algorithms for In-context Learning [36.895152458323764]
In this paper, we focus on efficient unlearning methods for the task adaptation phase of a pretrained large language model.
We observe that an LLM's ability to do in-context learning for task adaptation allows for efficient exact unlearning of task adaptation training data.
We propose a new holistic measure of unlearning cost which accounts for varying inference costs.
arXiv Detail & Related papers (2024-02-01T16:43:04Z) - Machine unlearning through fine-grained model parameters perturbation [26.653596302257057]
We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning strategies.
We also tackle the challenge of evaluating the effectiveness of machine unlearning.
arXiv Detail & Related papers (2024-01-09T07:14:45Z) - Unlearn What You Want to Forget: Efficient Unlearning for LLMs [92.51670143929056]
Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data.
This process might suffer from privacy issues and violations of data protection regulations.
We propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals.
arXiv Detail & Related papers (2023-10-31T03:35:59Z) - Machine Unlearning of Features and Labels [72.81914952849334]
We propose first scenarios for unlearning and labels in machine learning models.
Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
arXiv Detail & Related papers (2021-08-26T04:42:24Z)
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.