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Anonymizing data with relational and transaction attributes

Poulis, Giorgos, Loukides, Grigorios ORCID:, Gkoulalas-Divanis, Aris and Skiadopoulos, Spiros 2013. Anonymizing data with relational and transaction attributes. Presented at: ECML PKDD 2013: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Prague, Czech Republic, 23-27 September 2013. Published in: Blockeel, H., Kersting, K., Nijssen, S. and Zelezny, F. eds. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III. Lecture Notes in Computer Science. , vol.8190 Berlin and Heidelberg: Springer, pp. 353-369. 10.1007/978-3-642-40994-3_23

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Publishing datasets about individuals that contain both relational and transaction (i.e., set-valued) attributes is essential to support many applications, ranging from healthcare to marketing. However, preserving the privacy and utility of these datasets is challenging, as it requires (i) guarding against attackers, whose knowledge spans both attribute types, and (ii) minimizing the overall information loss. Existing anonymization techniques are not applicable to such datasets, and the problem cannot be tackled based on popular, multi-objective optimization strategies. This work proposes the first approach to address this problem. Based on this approach, we develop two frameworks to offer privacy, with bounded information loss in one attribute type and minimal information loss in the other. To realize each framework, we propose privacy algorithms that effectively preserve data utility, as verified by extensive experiments.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer
ISBN: 9783642409943
ISSN: 0302-9743
Last Modified: 25 Oct 2022 09:38

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