Data privacy against composition attack
Paper
Paper/Presentation Title | Data privacy against composition attack |
---|---|
Presentation Type | Paper |
Authors | Baig, Muzammil M. (Author), Li, Jiuyong (Author), Liu, Jixue (Author), Ding, Xiaofeng (Author) and Wang, Hua (Author) |
Editors | Lee, Sang-Goo, Peng, Zhiyong, Zhou, Xiaofang, Moon, Yang-Sae, Unland, Rainer and Yoo, Jaesoo |
Journal or Proceedings Title | Proceedings of the 17th International Conference on Database Systems for Advanced Applications (DASFAA 2012) |
ERA Conference ID | 42694 |
Number of Pages | 15 |
Year | 2012 |
Place of Publication | Berlin, Germany |
ISBN | 9783642290374 |
9783642290381 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-29038-1_24 |
Web Address (URL) of Paper | http://www.springerlink.com/content/9m5tgq185t71n5k7/fulltext.pdf |
Conference/Event | 17th International Conference on Database Systems for Advanced Applications (DASFAA 2012) |
Database Systems for Advanced Applications | |
Event Details | Database Systems for Advanced Applications DASFAA Rank A A |
Event Details | 17th International Conference on Database Systems for Advanced Applications (DASFAA 2012) Event Date 15 to end of 18 Apr 2012 Event Location Busan, South Korea |
Abstract | Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data holder. However, no method has been proposed for the anonymization scenario of multiple independent data publishing. A data holder publishes a dataset, which contains overlapping population with other datasets published by other independent data holders. No existing methods are able to protect privacy in such multiple independent data publishing. In this paper we propose a new generalization principle (ρ,α)-anonymization that effectively overcomes the privacy concerns for multiple independent data publishing. We also develop an effective algorithm to achieve the (ρ,α)-anonymization. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves high quality data utility. |
Keywords | composition attack; data anonymity; privacy; population statistics |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
460401. Cryptography | |
460499. Cybersecurity and privacy not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of South Australia |
Department of Mathematics and Computing | |
Institution of Origin | University of Southern Queensland |
Funding source | Australian Research Council (ARC) Grant ID DP0774450 |
Funding source | Australian Research Council (ARC) Grant ID DP110103142 |
https://research.usq.edu.au/item/q15q5/data-privacy-against-composition-attack
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