Utility-based anonymisation for dataset with multiple sensitive attributes. (2016)

• Record Type: Journal Article
• Authors: Wang, Lixia; Zhu, Qing
• Is Part Of: International journal of high performance computing and networking. Volume 9:Number 5/6(2016)
• Page Start: 401
• Page End: 408
• Publication Date: 2016
• Subjects: utility matrix -- anonymisation -- k-anonymity -- multiple attributes -- sensitive attributes -- privacy preservation -- privacy protection -- data publication -- greedy strategy -- equivalence class -- information loss -- diversity; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

Privacy-preserving data publication problem has attracted more and more attentions in recent years. A lot of related research works have been done towards dataset with single sensitive attribute. However, usually, original dataset contains more than one sensitive attribute. In this paper, we apply k-anonymity principle to solve the data publication problem for dataset with multiple sensitive attributes. We first cluster sensitive values based on a utility matrix. Then, we use a greedy strategy to partition tuples into equivalence classes. Our method can guarantee that the size of equivalence class is k except the last one, which reduces information loss. Also, we can guarantee the diversity of sensitive value in an equivalence class, which can protect privacy against the homogeneity attack. Experiments on a real dataset show that our method performs well on information loss, which indicates that we can guarantee data utility while protecting personal privacy.