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On the Protection of Private Information in Machine Learning Systems: Two Recent Approches (Invited Paper)

机译:关于在机器学习系统中保护私人信息:最近的两个批准(邀请纸)

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摘要

The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in the 1970s.
机译:最近,机器学习的显着增长导致了对机器学习依赖的数据的隐私的兴趣,以及用于保护隐私的新技术。但是,关于隐私的旧思想可能会保持有效和有用。本说明评论鉴于一些早期文献的智慧,特别是纯粹的智慧,特别是20世纪70年代盐醉者和施罗德蒸馏的原则。

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