Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 and Gkoulalas-Divanis, Aris 2012. Utility-preserving transaction data anonymization with low information loss. Expert Systems with Applications 39 (10) , pp. 9764-9777. 10.1016/j.eswa.2012.02.179 |
Abstract
Transaction data record various information about individuals, including their purchases and diagnoses, and are increasingly published to support large-scale and low-cost studies in domains such as marketing and medicine. However, the dissemination of transaction data may lead to privacy breaches, as it allows an attacker to link an individual’s record to their identity. Approaches that anonymize data by eliminating certain values in an individual’s record or by replacing them with more general values have been proposed recently, but they often produce data of limited usefulness. This is because these approaches adopt value transformation strategies that do not guarantee data utility in intended applications and objective measures that may lead to excessive data distortion. In this paper, we propose a novel approach for anonymizing data in a way that satisfies data publishers’ utility requirements and incurs low information loss. To achieve this, we introduce an accurate information loss measure and an effective anonymization algorithm that explores a large part of the problem space. An extensive experimental study, using click-stream and medical data, demonstrates that our approach permits many times more accurate query answering than the state-of-the-art methods, while it is comparable to them in terms of efficiency.
Item Type: | Article |
---|---|
Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Uncontrolled Keywords: | Anonymization; Transaction data; Data utility; Information loss |
Publisher: | Elsevier |
ISSN: | 0957-4174 |
Last Modified: | 20 Oct 2022 09:23 |
URI: | https://orca.cardiff.ac.uk/id/eprint/31765 |
Citation Data
Cited 35 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
Edit Item |