Exploiting contextual information in attacking set-generalized transactions

Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 and Ong, Hoang 2017. Exploiting contextual information in attacking set-generalized transactions. ACM Transactions on Internet Technology 17 (4) , 40. 10.1145/3106165

Preview

PDF - Accepted Post-Print Version Download (1MB) | Preview

Abstract

Transactions are records that contain a set of items about individuals. For example, items browsed by a customer when shopping online form a transaction. Today, many activities are carried out on the Internet, resulting in a large amount of transaction data being collected. Such data are often shared and analyzed to improve business and services, but they also contain private information about individuals that must be protected. Techniques have been proposed to sanitize transaction data before their release, and set-based generalization is one such method. In this article, we study how well set-based generalization can protect transactions. We propose methods to attack set-generalized transactions by exploiting contextual information that is available within the released data. Our results show that set-based generalization may not provide adequate protection for transactions, and up to 70% of the items added into the transactions during generalization to obfuscate original data can be detected by our methods with a precision over 80%.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	Copyright is held by the owner/author(s). Publication rights licensed to ACM
Publisher:	Association for Computing Machinery (ACM)
ISSN:	1533-5399
Date of First Compliant Deposit:	15 June 2017
Date of Acceptance:	1 May 2017
Last Modified:	07 Nov 2023 04:05
URI:	https://orca.cardiff.ac.uk/id/eprint/101465

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item