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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017

Autores

Mendes, R (Autor) Outra; João P. Vilela (Autor) Outra

Revista

Título: IEEE Access

Vol. 5

ISSN: 2169-3536

Editora: IEEE

Indexação

Scopus - 198 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica