Machine Learning (ML) models contain private information, and implementing the right to be forgotten is a challenging privacy issue in many data applications. Machine unlearning has emerged as an alternative to remove sensitive data from a trained model, but completely retraining ML models is often not feasible. This survey provides a concise appraisal of Machine Unlearning techniques, encompassing both exact and approximate methods, probable attacks, and verification approaches. The survey compares the merits and limitations each method and evaluates their performance using the Deltagrad exact machine unlearning method. The survey also highlights challenges like the pressing need for a robust model for non-IID deletion to mitigate fairness issues. Overall, the survey provides a thorough synopsis of machine unlearning techniques and applications, noting future research directions in this evolving field. The survey aims to be a valuable resource for researchers and practitioners seeking to provide privacy and equity in ML systems.

翻译：机器学习模型包含私有信息，实现被遗忘权是许多数据应用中一项具有挑战性的隐私问题。机器反学习已成为从训练模型中移除敏感数据的一种替代方案，但完全重新训练机器学习模型通常不可行。本综述对机器反学习技术进行了简明评估，涵盖精确方法与近似方法、潜在攻击手段及验证方法。综述比较了每种方法的优缺点，并利用Deltagrad精确机器反学习方法评估其性能。同时，本综述强调了挑战，例如急需一个能够处理非独立同分布数据删除的鲁棒模型以缓解公平性问题。总体而言，本综述提供了对机器反学习技术及其应用的全面概述，指出了这一不断发展领域中的未来研究方向。本综述旨在为致力于在机器学习系统中提供隐私与公平的研究人员和从业者提供宝贵资源。