Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Proceedings of the 16th international conference on World Wide Web
Measuring Topological Anonymity in Social Networks
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
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
Note: Symmetry in complex networks
Discrete Applied Mathematics
Efficiently indexing shortest paths by exploiting symmetry in graphs
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Utility-oriented K-anonymization on 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
LORA: link obfuscation by randomization in graphs
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
On the privacy and utility of anonymized social networks
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Combining ranking concept and social network analysis to detect collusive groups in online auctions
Expert Systems with Applications: An International Journal
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
A clustering approach for structural k-anonymity in social networks using genetic algorithm
Proceedings of the CUBE International Information Technology Conference
Anonymizing Subsets of Social Networks with Degree Constrained Subgraphs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
STK-anonymity: k-anonymity of social networks containing both structural and textual information
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
Privacy preserving release of blogosphere data in the presence of search engines
Information Processing and Management: an International Journal
GASNA: greedy algorithm for social network anonymization
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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With more and more social network data being released, protecting the sensitive information within social networks from leakage has become an important concern of publishers. Adversaries with some background structural knowledge about a target individual can easily re-identify him from the network, even if the identifiers have been replaced by randomized integers(i.e., the network is naively-anonymized). Since there exists numerous topological information that can be used to attack a victim's privacy, to resist such structural re-identification becomes a great challenge. Previous works only investigated a minority of such structural attacks, without considering protecting against re-identification under any potential structural knowledge about a target. To achieve this objective, in this paper we propose k-symmetry model, which modifies a naively-anonymized network so that for any vertex in the network, there exist at least k -- 1 structurally equivalent counterparts. We also propose sampling methods to extract approximate versions of the original network from the anonymized network so that statistical properties of the original network could be evaluated. Extensive experiments show that we can successfully recover a variety of such properties of the original network through aggregations on quite a small number of sample graphs.