Data privacy with R

Daniel Abril; Guillermo Navarro-Arribas; Vicenç Torra

doi:10.1007/978-3-319-09885-2_5

Data privacy with R

Daniel Abril, Guillermo Navarro-Arribas, Vicenç Torra

Research output: Contribution to journal › Article › Research › peer-review

Abstract

© Springer International Publishing Switzerland 2015. Privacy 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.

Original language	English
Pages (from-to)	63-82
Journal	Studies in Computational Intelligence
Volume	567
DOIs	https://doi.org/10.1007/978-3-319-09885-2_5
Publication status	Published - 1 Jan 2014

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Abril, D., Navarro-Arribas, G., & Torra, V. (2014). Data privacy with R. Studies in Computational Intelligence, 567, 63-82. https://doi.org/10.1007/978-3-319-09885-2_5

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