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Exploiting contextual information in attacking set-generalized transactions

By Jianhua Shao and Hoang Ong

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%

Publisher: 'Association for Computing Machinery (ACM)'
Year: 2017
DOI identifier: 10.1145/3106165
OAI identifier: oai:http://orca.cf.ac.uk:101465

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