Deep Learning on Private Data

被引：0

作者：

Fasano, Andrew ^{[1
,2
]}

Leek, Tim ^{[1
]}

Dolan-Gavitt, Brendan ^{[3
]}

Bundt, Josh ^{[2
,4
]}

机构：

[1] MIT Lincoln Lab, Lexington, MA 02421 USA

[2] Northeastern Univ, Boston, MA 02115 USA

[3] NYU, New York, NY 10003 USA

[4] US Army, Cyber Inst, Washington, DC 20310 USA

来源：

IEEE SECURITY & PRIVACY | 2019年 / 17卷 / 06期

关键词：

Training; Protocols; Cryptography; Servers; Computational modeling; Data models; Logic gates;

D O I：

10.1109/MSEC.2019.2933682

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Emerging complex deep neural networks require vast amounts of data to achieve high precision. However, the information is often collected from user logs and personal data. In this article, we summarize recent cryptographic methodologies for provably privacy-preserving deep learning and inference.

引用

页码：84 / 88

页数：5

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