Anonymization of multiple and personalized sensitive attributes

Lin, Jerry Chun-Wei and Liu, Qiankun and Fournier-Viger, Philippe and Djenouri, Youcef and Zhang, Ji (2018) Anonymization of multiple and personalized sensitive attributes. In: 20th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2018), 3-6 Sept 2018, Regensburg, Germany.


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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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © Springer Nature Switzerland AG 2018.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 - 31 Mar 2020)
Date Deposited: 23 Jul 2020 04:28
Last Modified: 30 Sep 2020 22:38
Uncontrolled Keywords: anonymization; cluster; multiple sensitive information; hierarchical attributes
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/978-3-319-98539-8_16
URI: http://eprints.usq.edu.au/id/eprint/38116

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