Anonymization of multiple and personalized sensitive attributes
Paper
Lin, Jerry Chun-Wei, Liu, Qiankun, Fournier-Viger, Philippe, Djenouri, Youcef and Zhang, Ji. 2018. "Anonymization of multiple and personalized sensitive attributes." Ordonez, Carlos and Bellatreche, Ladjel (ed.) 20th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2018). Regensburg, Germany 03 - 06 Sep 2018 Cham, Switzerland. https://doi.org/10.1007/978-3-319-98539-8_16
Paper/Presentation Title | Anonymization of multiple and personalized sensitive attributes |
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Presentation Type | Paper |
Authors | Lin, Jerry Chun-Wei (Author), Liu, Qiankun (Author), Fournier-Viger, Philippe (Author), Djenouri, Youcef (Author) and Zhang, Ji (Author) |
Editors | Ordonez, Carlos and Bellatreche, Ladjel |
Journal or Proceedings Title | International Conference on Big Data Analytics and Knowledge Discovery (DaWaK) |
ERA Conference ID | 72889 |
Journal Citation | 11031, pp. 204-215 |
Number of Pages | 12 |
Year | 2018 |
Place of Publication | Cham, Switzerland |
ISBN | 9783319985381 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-98539-8_16 |
Conference/Event | 20th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2018) |
International Conference on Big Data Analytics and Knowledge Discovery (DaWaK) | |
Event Details | 20th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2018) Event Date 03 to end of 06 Sep 2018 Event Location Regensburg, Germany |
Event Details | International Conference on Big Data Analytics and Knowledge Discovery (DaWaK) DaWaK |
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. |
Keywords | anonymization; cluster; multiple sensitive information;hierarchical attributes |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | © Springer Nature Switzerland AG 2018. |
Byline Affiliations | Harbin Institute of Technology, China |
University of Southern Denmark, Denmark | |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
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