Recent Developments in Privacy-preserving Mining of Clinical Data

被引：2

作者：

Desmet, Chance ^{[1
]}

Cook, Diane J. ^{[1
]}

机构：

[1] Washington State University, P.O. Box 642752, Pullman,WA,99164- 2752, United States

来源：

ACM/IMS Transactions on Data Science | 2021年 / 2卷 / 04期

关键词：

Clinical application - Clinical data - Clinical PPDM - Large amounts of data - Personal information - PPDM - Privacy - Privacy preserving - Privacy-preserving data mining - Recent researches;

D O I：

10.1145/3447774

中图分类号：

学科分类号：

摘要：

With the dramatic improvements in both the capability to collect personal data and the capability to analyze large amounts of data, increasingly sophisticated and personal insights are being drawn. These insights are valuable for clinical applications but also open up possibilities for identification and abuse of personal information. In this article, we survey recent research on classical methods of privacy-preserving data mining. Looking at dominant techniques and recent innovations to them, we examine the applicability of these methods to the privacy-preserving analysis of clinical data. We also discuss promising directions for future research in this area. © 2021 Association for Computing Machinery.

引用

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