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

• Autor(a) principal: Mendes, Ricardo
• Data de Publicação: 2017
• Outros Autores: Vilela, João P.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/102068; https://doi.org/10.1109/ACCESS.2017.2706947
• topic: privacy; data mining; privacy-preserving data mining; metrics; knowledge extraction

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 bene cially to the society in many different elds. However, this storage and ow 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 elds. Furthermore, the current challenges and open issues in PPDM are discussed.