Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 and Ong, Hoang 2016. Semantic attack on anonymised transactions. Presented at: FDSE 2014: 1st International Conference on Future Data and Security Engineering 2014, Ho Chi Minh City, Vietnam, 19-21 November 2014. Published in: Hameurlain, Abdelkader, Küng, Josef, Wagner, Roland, Khanh Dang, Tran and Thoai, Nam eds. Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIII: Selected Papers from FDSE 2014. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.9480 Springer, pp. 75-99. 10.1007/978-3-662-49175-1_4 |
Abstract
A transaction is a data record that contains items associated with an individual. For example, a set of movies rated by an individual form a transaction. Transaction data are important to applications such as marketing analysis and medical studies, but they may contain sensitive information about individuals which must be sanitised before being used. One popular approach to anonymising transaction data is set-based generalisation, which attempts to hide an original item by replacing it with a set of items. In this paper, we study how well this method can protect transaction data. We propose an attack that aims to reconstruct original transaction data from its set-generalised version by analysing semantic relationships that exist among the items. Our experiments show that set-based generalisation may not provide adequate protection for transaction data, and about 50 % of the items added to the transactions during generalisation can be detected by our method with a precision greater than 80 %.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Publisher: | Springer |
ISBN: | 9783662491744 |
ISSN: | 0302-9743 |
Date of Acceptance: | 1 January 2016 |
Last Modified: | 01 Nov 2022 09:29 |
URI: | https://orca.cardiff.ac.uk/id/eprint/88107 |
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