Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
LORA: link obfuscation by randomization in graphs
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
Limiting link disclosure in social network analysis through subgraph-wise perturbation
Proceedings of the 15th International Conference on Extending Database Technology
EWNI: efficient anonymization of vulnerable individuals in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Injecting uncertainty in graphs for identity obfuscation
Proceedings of the VLDB Endowment
Sensitive label privacy protection on social network data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Delineating social network data anonymization via random edge perturbation
Proceedings of the 21st ACM international conference on Information and knowledge management
Preserving privacy and frequent sharing patterns for social network data publishing
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Analyzing the structure of social networks is of interest in a wide range of disciplines, but such activity is limited by the fact that these data represent sensitive information and can not be published in their raw form. One of the approaches to sanitize network data is to randomly add or remove edges from the graph. Recent studies have quantified the level of anonymity that is obtained by random perturbation by means of a-posteriori belief probabilities and, by conducting experiments on small datasets, arrived at the conclusion that random perturbation can not achieve meaningful levels of anonymity without deteriorating the graph features.