Privacy-Preserving Data Mining: Methods, Metrics, and Applications

被引：152

作者：

Mendes, Ricardo ^{[1
]}

Vilela, Joao P. ^{[1
]}

机构：

[1] Univ Coimbra, Dept Informat Engn, CISUC, P-3004504 Coimbra, Portugal

来源：

IEEE ACCESS | 2017年 / 5卷

关键词：

Survey; privacy; data mining; privacy-preserving data mining; metrics; knowledge extraction; OF-THE-ART; DIFFERENTIAL PRIVACY; ASSOCIATION RULES; LOCATION PRIVACY; DATA AGGREGATION; HEALTH RECORDS; PROTECTION; PROTOCOL; MODEL; PRESERVATION;

D O I：

10.1109/ACCESS.2017.2706947

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining ( PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed.

引用

页码：10562 / 10582

页数：21

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