文章基本信息

标题：An Anonymization Algorithm for (α, β, γ, δ)-Social Network Privacy Considering Data Utility
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作者：Mehri Rajaei ; Mostafa S. Haghjoo ; Eynollah Khanjari Miyaneh 等
期刊名称：Journal of Universal Computer Science
印刷版ISSN：0948-6968
出版年度：2015
卷号：21
期号：2
页码：268-305
出版社：Graz University of Technology and Know-Center
摘要：A well-known privacy-preserving network data publication problem focuses on how to publish social network data while protecting privacy and permitting useful analysis. Designing algorithms that safely transform network data is an active area of research. The process of applying these transformations is called anonymization operation. The authors recently proposed the (?,?,?,?)-SNP (Social Network Privacy) model and its an anonymization technique. The present paper introduces a novel anonymization algorithm for the (?,?,?,?)-SNP model. The desirability metric between two individuals of social network is defined to show the desirability of locating them in one group keeping in mind privacy and data utility considerations. Next, individuals are grouped using a greedy algorithm based on the values of this metric. This algorithm tries to generate small-sized groups by maximizing the sum of desirability values between members of each group. The proposed algorithm was tested using two real datasets and one synthetic dataset. Experimental results show satisfactory data utility for topological, spectrum and aggregate queries on anonymized data. The results of the proposed algorithm were compared in the topological properties with results of two recently proposed anonymization schemes: Subgraph-wise Perturbation (SP) and Neighborhood Randomization (NR). The results show that the proposed method is better than or similar to SP and NR for preservation of all structural and spectrum properties, except for the clustering coefficient.