Privacy-Preserving Machine Learning: Threats and Solutions

被引：211

作者：

Al-Rubaie, Mohammad ^{[1
]}

Chang, J. Morris ^{[2
]}

机构：

[1] Iowa State Univ, Comp Engn, Ames, IA 50011 USA

[2] Univ S Florida, Dept Elect Engn, Tampa, FL USA

来源：

IEEE SECURITY & PRIVACY | 2019年 / 17卷 / 02期

关键词：

SYSTEMS;

D O I：

10.1109/MSEC.2018.2888775

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For privacy concerns to be addressed adequately in today's machine-learning (ML) systems, the knowledge gap between the ML and privacy communities must be bridged. This article aims to provide an introduction to the intersection of both fields with special emphasis on the techniques used to protect the data.

引用

页码：49 / 58

页数：10

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