机器学习:需求与实现策略

Aman Tahiliani, Vikas Hassija, V. Chamola, M. Guizani
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引用次数: 6

摘要

一般来说,当用户在一些在线平台上分享自己的信息时,他们有意或无意地允许这些数据被这些公司背后的公司用于各种目的,包括将这些信息出售给广告商,以及利用这些信息更好地丰富他们的预测模型。如果用户改变主意,允许公司使用这些关于他们的数据,那么公司要摆脱这些收集到的数据的影响就变成了一项艰巨的任务,特别是当这些数据被用来训练他们的机器学习模型时。欧盟等管理机构最近的立法赋予人们选择有关他们的数据在哪里使用的权利,包括有权从公司的数据库和机器学习模型中完全删除他们的数据及其由此产生的影响。为了能够大规模地做到这一点,需要发明新的机器学习解决方案。在本文中,我们研究了一些已经提出的机器学习策略的早期模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Unlearning: Its Need and Implementation Strategies
Generally when users share information about themselves on some online platforms, they knowingly or unknowingly allow this data to be used by the companies behind these companies for various purposes including selling this information to advertisers as well as using it to better enrich their predictive models. In the event of the user changing their minds on allowing such data about them to be able to be used by the companies, it becomes a strenuous task for the companies to get rid of the influence of this collected data, especially when it has been used to train their machine learning models. Recent legislations by governing bodies, like the European Union, grant people the right to choose where data about them may be used, including a right to have their data and its resulting influence be completely removed from a company’s databases and machine learning models. To be able to do this at scale new machine unlearning solutions need to be invented. In this paper, we look at some of these early models of machine unlearning strategies that have been proposed.
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