Anonymization of multiple and personalized sensitive attributes

Lin, Jerry Chun-Wei and Liu, Qiankun and Fournier-Viger, Philippe and Djenouri, Youcef and Zhang, Ji ORCID: (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.


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
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