A graph-based multifold model for anonymizing data with attributes of multiple types. Issue 72 (January 2018)

• Record Type: Journal Article
• Authors: Wang, Li-E.; Li, Xianxian
• Is Part Of: Computers & security. Issue 72(2018)
• Page Start: 122
• Page End: 135
• Publication Date: 2018-01
• Subjects: Privacy protection -- Data publishing -- Transactional data -- Uncertain graph -- High-dimensional data -- Anonymization; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2017.09.003
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: Transactional data with attributes of multiple types may be extremely useful to secondary analysis (e.g., learning models and finding patterns). However, anonymization of such data is challenging because it contains multiple types of attributes (e.g., relational and set-valued attributes). Existing privacy-preserving techniques are not applicable to address this problem. In this paper, we propose a novel graph-based multifold model to anonymize data with attributes of multiple types. Under this model, such data are modelled as a graph, and multifold privacy is guaranteed through fuzzing on sensitive attributes and converting associations among items into an uncertain form. Specifically, we define a multi-objective attack model in a graph and devise a safety parameter and algorithm to prevent such attacks. Experiments have been performed on real-life data sets to evaluate the performance.