期刊
IEEE ACCESS
卷 5, 期 -, 页码 10562-10582出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2706947
关键词
Survey; privacy; data mining; privacy-preserving data mining; metrics; knowledge extraction
资金
- project SWING2 - Fundos Europeus Estruturais e de Investimento (FEEI) through Programa Operacional Competitividade e Internacionalizacao-COMPETE [PTDC/EEITEL/3684/2014]
- FCT - Fundos Europeus Estruturais e de Investimento [POCI-01-0145-FEDER-016753]
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.
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