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Data Science LandscapeData science landscape : Tracking the Ecosystem Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Privacy Preserving Data Mining Techniques for Hiding Sensitive Data: A Step Towards Open Data

Author : Durga Toshniwal

Published in: Data Science Landscape

Publisher: Springer Singapore

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Abstract

Privacy Preserving Data Mining (PPDM) is an area that deals with data mining techniques that allow the data to be mined while keeping its privacy intact. PPDM comes into play when different parties want to involve in collaborative data mining and wish to collectively mine their data to extract knowledge while keeping their data private. The preservation of privacy of data is also of utmost importance when we want to publish any data as open data. This study includes a detailed review of some of the recent techniques proposed in this area. The biggest challenge in devising any privacy preserving data mining technique is to achieve a good balance between privacy and utility of the privacy preserved data.

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Metadata
Title
Privacy Preserving Data Mining TechniquesPrivacy Preserving Data Mining Techniques for Hiding Sensitive DataHiding Sensitive Data : A Step Towards Open DataOpen Data
Author
Durga Toshniwal
Copyright Year
2018
Publisher
Springer Singapore
Book
Data Science Landscape

Print ISBN: 978-981-10-7514-8

Electronic ISBN: 978-981-10-7515-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7515-5_15

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