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Title

Data Privacy with R

AuthorsAbril, Daniel; Navarro-Arribas, Guillermo; Torra, Vicenç
KeywordsDisclosure risk
Data privacy
PPDM
Privacy Preserving Data Mining
Issue Date2015
PublisherSpringer
CitationAdvanced Research in Data Privacy, Studies in Computational Intelligence, vol. 567: 63-82 (2015).
AbstractPrivacy Preserving Data Mining (PPDM) is an application field, which is becoming very relevant. Its goal is the study of new mechanisms which allow the dissemination of confidential data for data mining tasks while preserving individual private information. Additionally, due to the relevance of R language in the statistics and data mining communities, it is undoubtedly a good environment to research, develop and test privacy techniques aimed to data mining. In this chapter we outline some helpful tools in R to introduce readers to that field, so that we present several PPDM protection techniques as well as their information loss and disclosure risk evaluation process and outline some tools in R to help to introduce practitioners to this field. © Springer International Publishing Switzerland 2015.
URIhttp://hdl.handle.net/10261/130619
DOI10.1007/978-3-319-09885-2_5
Identifiersdoi: 10.1007/978-3-319-09885-2_5
issn: 1860949X
Appears in Collections:(IIIA) Libros y partes de libros
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