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

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Mendes, R (Author) Other; João P. Vilela (Author) Other

Journal

Title: IEEE Access

Vol. 5

ISSN: 2169-3536

Publisher: IEEE

Indexing

Scopus - 220 Citations

Other information

Authenticus ID: P-00N-ZB9

DOI: 10.1109/access.2017.2706947

Abstract (EN): 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. © 2017 IEEE.

Language: English

Type (Professor's evaluation): Scientific