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- W96371004 abstract "Social network data are generally published in the form of social graphs which are being used for extensive scientific research. We have noticed that even a k-degree anonymization of social graph can’t ensure protection against identity disclosure. In this paper, we have discussed how closeness centrality measure can be used to identify a social entity in the presence of kdegree anonymization. We have proposed a new model called k-degree closeness anonymization by adopting a mixed strategy of k-degree anonymity, degree centrality and closeness centrality. The model has two phases, namely, construction and validation. The construction phase transforms a graph with given sequence to a graph with anonymous sequence in such a manner that the closeness centrality measure is distributed among the nodes in a smooth way. The nodes with the same degree centrality are assigned with a closer set of closeness centrality values, making re-identification difficult. Validation phase validates our model by generating 1-neighborhood graphs. Algorithms have been developed both for the construction and validation phases." @default.
- W96371004 created "2016-06-24" @default.
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- W96371004 date "2015-01-01" @default.
- W96371004 modified "2023-10-16" @default.
- W96371004 title "k-degree Closeness Anonymity: A Centrality Measure Based Approach for Network Anonymization" @default.
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- W96371004 doi "https://doi.org/10.1007/978-3-319-14977-6_29" @default.
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