arXiv 2411.171262024-12-03cs.LG

从机器学习到机器遗忘：在维护预测模型商业价值的同时遵守GDPR的被遗忘权

From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models

Shanghai University of Finance and Economics（上海财经大学）；Beihang University（北京航空航天大学）；Hefei University of Technology（合肥工业大学）；University of South Florida（南佛罗里达大学）；University of Maryland（马里兰大学）

Yuncong Yang, Xiao Han, Yidong Chai, Reza Ebrahimi, Rouzbeh Behnia, Balaji Padmanabhan

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AI总结：

本文提出ETID框架，通过集成学习与定制化蒸馏遗忘机制，在满足GDPR被遗忘权删除请求的同时保持预测模型性能与商业价值。

摘要：

近期的隐私法规（例如GDPR）赋予数据主体“被遗忘权”（RTBF），并要求公司履行数据主体提出的数据删除请求。然而，公司在遵守RTBF法规方面面临巨大挑战，尤其是在被要求从其训练良好的预测模型中删除特定训练数据时。尽管研究人员已经提出了旨在快速删除数据的机器遗忘方法，但这些方法往往忽视维持模型性能（例如准确率），这可能导致经济损失以及无法履行RTBF义务。本研究开发了一个从机器学习到遗忘的整体框架，称为基于集成的迭代信息蒸馏（ETID），以在实现高效数据删除的同时保留预测模型的商业价值。ETID纳入了一种新的集成学习方法，用于构建准确的预测模型，从而有助于处理数据删除请求。ETID还引入了一种创新的、基于蒸馏且针对所构建集成模型定制的遗忘方法，以实现高效且有效的数据删除。大量实验表明，ETID优于多种最先进方法，并能够高效交付高质量的已遗忘模型。我们还强调，ETID有潜力成为培育合法且繁荣的数据与预测服务市场的关键工具。

英文摘要：

Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulations, particularly when asked to erase specific training data from their well-trained predictive models. While researchers have introduced machine unlearning methods aimed at fast data erasure, these approaches often overlook maintaining model performance (e.g., accuracy), which can lead to financial losses and non-compliance with RTBF obligations. This work develops a holistic machine learning-to-unlearning framework, called Ensemble-based iTerative Information Distillation (ETID), to achieve efficient data erasure while preserving the business value of predictive models. ETID incorporates a new ensemble learning method to build an accurate predictive model that can facilitate handling data erasure requests. ETID also introduces an innovative distillation-based unlearning method tailored to the constructed ensemble model to enable efficient and effective data erasure. Extensive experiments demonstrate that ETID outperforms various state-of-the-art methods and can deliver high-quality unlearned models with efficiency. We also highlight ETID's potential as a crucial tool for fostering a legitimate and thriving market for data and predictive services.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。