Premium
A relative privacy model for effective privacy preservation in transactional data
Author(s) -
Bewong Michael,
Liu Jixue,
Liu Lin,
Li Jiuyong,
Choo KimKwang Raymond
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4923
Subject(s) - computer science , data publishing , metric (unit) , transactional leadership , adversary , benchmark (surveying) , inference , focus (optics) , information privacy , publishing , transaction data , point (geometry) , computer security , data science , data mining , internet privacy , database , artificial intelligence , database transaction , social psychology , physics , geometry , geodesy , optics , mathematics , psychology , political science , law , geography , operations management , economics
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.