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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Graph Anonymization Using Hierarchical Clustering
verfasst von : Debasis Mohapatra, Manas Ranjan Patra
Erschienen in: Computational Intelligence in Data Mining
Verlag: Springer Singapore
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Abstract
Privacy preserving data publication of social network is an emerging trend that focuses on the dual concerns of information privacy and utility. Privacy preservation is essential in social networks as social networks are abundant source of information for studying the behavior of the social entities. Social network disseminates its information through social graph. Anonymization of social graph is essential in data publication to preserve the privacy of participating social entities. In this paper, we propose a hierarchical clustering-based approach for k-degree anonymity. The attack model focuses on identity disclosure problem. Our approach unlike other approach discussed in Liu and Terzi (Proceedings of ACM SIGMOD, 2008, [1]) generates k-degree anonymous sequence with the k value. Havel–Hakimi algorithm is used to check the sequence is graphic or not. Subsequently, the construction phase takes place with the help of edge addition operation.