PPMLP 2020: Workshop on Privacy-Preserving Machine Learning in Practice

被引：1

作者：

Zhang, Benyu ^{[1
]}

Zaharia, Matei ^{[2
]}

Ji, Shouling ^{[3
]}

Popa, Raluca Ada ^{[4
]}

Gu, Guofei ^{[5
]}

机构：

[1] Ant Grp, SCI Lab, Hangzhou, Peoples R China

[2] Stanford Univ, Dept CS, Stanford, CA 94305 USA

[3] Zhejiang Univ, Coll CST, Hangzhou, Peoples R China

[4] Univ Calif Berkeley, Dept EECS, Berkeley, CA USA

[5] Texas A&M Univ, Dept CSE, College Stn, TX 77843 USA

来源：

CCS '20: PROCEEDINGS OF THE 2020 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY | 2020年

关键词：

Machine Learning; MPC; Differential Privacy;

D O I：

10.1145/3372297.3416245

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the rapid development of technology, data is becoming ubiquitous. User privacy and data security are drawing much attention over the recent years, especially with the European Union's General Data Protection Regulation (GDPR) and other laws coming into force. On one hand, from the customers' perspective, how to protect user privacy while making use of customers? data is a challenging task. On the other hand, data silos are becoming one of the most prominent issues for the society. From the business? perspective, how to bridge these isolated data islands to build better AI systems while meeting the data privacy and regulatory compliance requirements has imposed great challenges to the traditional machine learning paradigm. PPMLP will provide an opportunity to connect researchers from both CCS community and machine learning community to tackle these challenges.

引用

页码：2139 / 2140

页数：2