Preserving Privacy in Data Analytics

被引：0

作者：

Ghemri, Lila ^{[1
]}

机构：

[1] Texas Southern Univ, Houston, TX 77004 USA

来源：

PROCEEDINGS OF THE ACM INTERNATIONAL WORKSHOP ON SECURITY AND PRIVACY ANALYTICS (IWSPA '19) | 2019年

关键词：

Privacy-preserving methods; privacy; data analytics; tutorial;

D O I：

10.1145/3309182.3311786

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Data Analytics is becoming an essential business tool for many data intensive companies and organizations. However, the increased use of such methods comes with the threat of data disclosure. Privacy-preserving methods have been developed with varying degrees of efficiency with the main goal of protecting individuals' privacy. This tutorial aims at presenting models and techniques of preserving privacy in machine learning and data mining.

引用

页码：3 / 4

页数：2

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