Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Class-based graph anonymization for social network data
Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
k-symmetry model for identity anonymization in social networks
Proceedings of the 13th International Conference on Extending Database Technology
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Personalized privacy protection in social networks
Proceedings of the VLDB Endowment
Resisting structural re-identification in anonymized social networks
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-aware data management in information networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Node protection in weighted social networks
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
A generalization based approach for anonymizing weighted social network graphs
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Limiting link disclosure in social network analysis through subgraph-wise perturbation
Proceedings of the 15th International Conference on Extending Database Technology
Sensitive label privacy protection on social network data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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We study the problem of anonymizing social networks to prevent individual identifications which use both structural (node degrees) and textual (edge labels) information in social networks. We introduce the concept of Structural and Textual (ST)-equivalence of individuals at two levels (strict and loose), and formally define the problem as Structure and Text aware K-anonymity of social networks (STK-Anonymity). In an STK-anonymized network, each individual is ST-equivalent to at least K-1 other nodes. The major challenge in achieving STK-Anonymity comes from the correlation of edge labels, which causes the propagation of edge anonymization. To address the challenge, we present a two-phase approach. In particular, a set-enumeration tree based approach and three pruning strategies are introduced in the second phase to avoid the propagation problem during anonymization. Experimental results on both real and synthetic datasets are presented to show the effectiveness and efficiency of our approaches.