提出增量遗忘技术，实现高效删除数据且不重训模型。
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
- 通过增量更新而非重训，快速移除特定数据
- 支持隐私保护下模型性能与可扩展性兼顾
- 适合关注数据合规与隐私安全的研究者
机器学习应用中对数据隐私的需求日益增长，促使机器遗忘（MU）成为研究热点。随着全球范围内‘被遗忘的权利’法规落地，亟需开发在不损害模型性能与可扩展性的前提下，从AI系统中删除用户数据的机制。增量遗忘（IU）是一种有前景的解决方案，能够在无需耗时昂贵的全量重训情况下，高效移除特定数据。本文综述了各类增量遗忘技术与方法，探讨其设计与实现中的挑战，讨论评估遗忘效果的数据集与指标，并提出潜在应对方案与未来研究方向。该综述为希望理解当前增量遗忘研究现状及其在隐私保护智能系统中潜力的研究人员和实践者提供重要参考。
原文摘要 · Abstract (English)
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' becomes regulated globally, it is increasingly important to develop mechanisms that delete user data from AI systems while maintaining performance and scalability of these systems. Incremental Unlearning (IU) is a promising MU solution to address the challenges of efficiently removing specific data from ML models without the need for expensive and time-consuming full retraining. This paper presents the various techniques and approaches to IU. It explores the challenges faced in designing and implementing IU mechanisms. Datasets and metrics for evaluating the performance of unlearning techniques are discussed as well. Finally, potential solutions to the IU challenges alongside future research directions are offered. This survey provides valuable insights for researchers and practitioners seeking to understand the current landscape of IU and its potential for enhancing privacy-preserving intelligent systems.
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