Privacy-preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

被引：7

作者：

Naresh, Vankamamidi S. ^{[1
,3
]}

Thamarai, Muthusamy ^{[2
]}

机构：

[1] Sri Vasavi Engn Coll, Dept Comp Sci & Engn, Tadepalligudem, Andhra Pradesh, India

[2] Sri Vasavi Engn Coll, Dept Elect & Commun Engn, Tadepalligudem, Andhra Pradesh, India

[3] SriVasavi Engn Coll, Dept Comp Sci & Engn, Tadepalligudem 534101, Andhra Pradesh, India

来源：

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY | 2023年 / 13卷 / 02期

关键词：

data privacy; healthcare; privacy-preserving computational techniques; data mining; machine learning; federated learning; CLOUD; SECURITY; ATTACKS; THREATS; CLASSIFICATION; ALGORITHMS; PROTECTION; NETWORKS; SCHEME;

D O I：

10.1002/widm.1490

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discuss different privacy-preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state-of-the-art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed. Finally, we present open challenges in these systems and future research directions.This article is categorized under:Application Areas > Health CareTechnologies > Machine LearningCommercial, Legal, and Ethical Issues > Security and Privacy

引用

页数：42

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