Query size estimation by adaptive sampling
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
ANF: a fast and scalable tool for data mining in massive graphs
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th international conference on World Wide Web
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
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
The union-split algorithm and cluster-based anonymization of social networks
Proceedings of the 4th International Symposium on Information, Computer, and Communications Security
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
k-symmetry model for identity anonymization in social networks
Proceedings of the 13th International Conference on Extending Database Technology
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Subgraph Patterns from Uncertain Graph Data
IEEE Transactions on Knowledge and Data Engineering
k-nearest neighbors in uncertain graphs
Proceedings of the VLDB Endowment
HADI: Mining Radii of Large Graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Resisting structural re-identification in anonymized social networks
The VLDB Journal — The International Journal on Very Large Data Bases
HyperANF: approximating the neighbourhood function of very large graphs on a budget
Proceedings of the 20th international conference on World wide web
A comparison of three algorithms for approximating the distance distribution in real-world graphs
TAPAS'11 Proceedings of the First international ICST conference on Theory and practice of algorithms in (computer) systems
Distance-constraint reachability computation in uncertain graphs
Proceedings of the VLDB Endowment
Identity obfuscation in graphs through the information theoretic lens
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Discovering highly reliable subgraphs in uncertain graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy Risk in Graph Stream Publishing for Social Network Data
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Anonymization of Centralized and Distributed Social Networks by Sequential Clustering
IEEE Transactions on Knowledge and Data Engineering
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Data collected nowadays by social-networking applications create fascinating opportunities for building novel services, as well as expanding our understanding about social structures and their dynamics. Unfortunately, publishing social-network graphs is considered an ill-advised practice due to privacy concerns. To alleviate this problem, several anonymization methods have been proposed, aiming at reducing the risk of a privacy breach on the published data, while still allowing to analyze them and draw relevant conclusions. In this paper we introduce a new anonymization approach that is based on injecting uncertainty in social graphs and publishing the resulting uncertain graphs. While existing approaches obfuscate graph data by adding or removing edges entirely, we propose using a finer-grained perturbation that adds or removes edges partially: this way we can achieve the same desired level of obfuscation with smaller changes in the data, thus maintaining higher utility. Our experiments on real-world networks confirm that at the same level of identity obfuscation our method provides higher usefulness than existing randomized methods that publish standard graphs.