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What is K-Anonymization in Social Network Clustering about?

The document proposes a k-anonymization approach for clustering graphs in social networks to protect sensitive user data. It aims to minimize information loss while maintaining privacy. The approach uses a greedy algorithm that assigns nodes to clusters to minimize a weighted information loss formula, balancing generalized and structural information loss. It allows users to tune this balance with a parameter. An empirical analysis shows the proposed approach reduces normalized structural information loss co

Author

Anston Layer

Language

EN