A relative privacy model for effective privacy preservation in transactional data

Michael Bewong, Jixue Liu, Lin Liu, Jiuyong Li, Kim-Kwang Raymond Choo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Data publishing is pivotal to advances in knowledge discovery. Nonetheless, such publishing may suffer from privacy disclosures. This is especially the case in transactional data such as web search and point of sales logs. The reason is that the current potent privacy preserving mechanisms mainly focus on relational data. In this work, we propose a new privacy metric for transactional data to prevent inference attacks by ensuring that the adversary learns no more about an intended victim than what is publicly available. We then propose a publication mechanism Anony, which satisfies our privacy metric without excessive loss of utility. Finally, we present an empirical evaluation of our method on three benchmark datasets, and the results show the effectiveness of our method.
Original languageEnglish
Article numbere4923
Pages (from-to)1-13
Number of pages13
JournalConcurrency and Computation: Practice and Experience
Volume31
Issue number23
DOIs
Publication statusPublished - 19 Sep 2018

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