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Título

Data Privacy with R

AutorAbril, Daniel; Navarro-Arribas, Guillermo; Torra, Vicenç
Palabras claveDisclosure risk
Data privacy
PPDM
Privacy Preserving Data Mining
Fecha de publicación2015
EditorSpringer
CitaciónAdvanced Research in Data Privacy, Studies in Computational Intelligence, vol. 567: 63-82 (2015).
ResumenPrivacy 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
Identificadoresdoi: 10.1007/978-3-319-09885-2_5
issn: 1860949X
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