Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage
- URL: http://arxiv.org/abs/2411.03914v1
- Date: Wed, 06 Nov 2024 13:47:04 GMT
- Title: Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage
- Authors: Hengzhu Liu, Tianqing Zhu, Lefeng Zhang, Ping Xiong,
- Abstract summary: Recent legislation obligates organizations to remove requested data and its influence from a trained model.
We propose a game-theoretic machine unlearning algorithm that simulates the competitive relationship between unlearning performance and privacy protection.
- Score: 12.737028324709609
- License:
- Abstract: With the extensive use of machine learning technologies, data providers encounter increasing privacy risks. Recent legislation, such as GDPR, obligates organizations to remove requested data and its influence from a trained model. Machine unlearning is an emerging technique designed to enable machine learning models to erase users' private information. Although several efficient machine unlearning schemes have been proposed, these methods still have limitations. First, removing the contributions of partial data may lead to model performance degradation. Second, discrepancies between the original and generated unlearned models can be exploited by attackers to obtain target sample's information, resulting in additional privacy leakage risks. To address above challenges, we proposed a game-theoretic machine unlearning algorithm that simulates the competitive relationship between unlearning performance and privacy protection. This algorithm comprises unlearning and privacy modules. The unlearning module possesses a loss function composed of model distance and classification error, which is used to derive the optimal strategy. The privacy module aims to make it difficult for an attacker to infer membership information from the unlearned data, thereby reducing the privacy leakage risk during the unlearning process. Additionally, the experimental results on real-world datasets demonstrate that this game-theoretic unlearning algorithm's effectiveness and its ability to generate an unlearned model with a performance similar to that of the retrained one while mitigating extra privacy leakage risks.
Related papers
- Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation [11.174705227990241]
Machine unlearning is a technique that removes specific data influences from trained models without the need for extensive retraining.
This paper presents a novel approach to machine unlearning by employing Layer-wise Relevance Analysis and Neuronal Path Perturbation.
Our method balances machine unlearning performance and model utility by identifying and perturbing highly relevant neurons, thereby achieving effective unlearning.
arXiv Detail & Related papers (2024-10-31T07:37:04Z) - Verification of Machine Unlearning is Fragile [48.71651033308842]
We introduce two novel adversarial unlearning processes capable of circumventing both types of verification strategies.
This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
arXiv Detail & Related papers (2024-08-01T21:37:10Z) - Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [49.043599241803825]
Iterative Contrastive Unlearning (ICU) framework consists of three core components.
A Knowledge Unlearning Induction module removes specific knowledge through an unlearning loss.
A Contrastive Learning Enhancement module to preserve the model's expressive capabilities against the pure unlearning goal.
And an Iterative Unlearning Refinement module that dynamically assess the unlearning extent on specific data pieces and make iterative update.
arXiv Detail & Related papers (2024-07-25T07:09:35Z) - 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) - 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) - 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) - Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of
Foundation Models [103.71308117592963]
We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning.
In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
arXiv Detail & Related papers (2022-11-27T21:43:45Z) - A Survey on Differential Privacy with Machine Learning and Future
Outlook [0.0]
differential privacy is used to protect machine learning models from any attacks and vulnerabilities.
This survey paper presents different differentially private machine learning algorithms categorized into two main categories.
arXiv Detail & Related papers (2022-11-19T14:20:53Z) - 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.