A relative privacy model for effective privacy preservation in transactional data. (19th September 2018)

• Record Type: Journal Article
• Authors: Bewong, Michael; Liu, Jixue; Liu, Lin; Li, Jiuyong; Choo, Kim‐Kwang Raymond
• Other Names: Chen Jinjun guestEditor.; Zheng Xianghan guestEditor.; Rong Chunming guestEditor.; Badarch Tuyatsetseg guestEditor.; Nanda Priyadarsi guestEditor.; Puthal Deepak guestEditor.; Mohanty Saraju P. guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 23(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-19
• Subjects: anonymity -- data publication -- privacy preservation -- transactional data; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4923
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.