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Graph BI & Analytics: Current State and Future Challenges Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Anonymization of Multiple and Personalized Sensitive Attributes

Authors : Jerry Chun-Wei Lin, Qiankun Liu, Philippe Fournier-Viger, Youcef Djenouri, Ji Zhang

Published in: Big Data Analytics and Knowledge Discovery

Publisher: Springer International Publishing

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Abstract

In the past, many algorithms have presented to hide the sensitive information but most of them identify the sensitive information as the same for all users/transactions, which is not a situation happened in realistic applications. In this paper, we present the (k, p)-anonymity framework to hide not only the multiple sensitive information but also the personal sensitive ones. Extensive experiments indicated that the proposed algorithm outperforms the-state-of-the-art algorithms in terms of information loss and runtime.

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Metadata
Title
Anonymization of Multiple and Personalized Sensitive Attributes
Authors
Jerry Chun-Wei Lin
Qiankun Liu
Philippe Fournier-Viger
Youcef Djenouri
Ji Zhang
Copyright Year
2018
Book
Big Data Analytics and Knowledge Discovery

Print ISBN: 978-3-319-98538-1

Electronic ISBN: 978-3-319-98539-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-98539-8_16

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