Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Proceedings of the 16th international conference on World Wide Web
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Link privacy in social networks
Proceedings of the 17th ACM conference on Information and knowledge management
On the Anonymization of Sparse High-Dimensional Data
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
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
The union-split algorithm and cluster-based anonymization of social networks
Proceedings of the 4th International Symposium on Information, Computer, and Communications Security
A brief survey on anonymization techniques for privacy preserving publishing of social network data
ACM SIGKDD Explorations Newsletter
Anonymized data: generation, models, usage
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Preserving Privacy in Social Networks: A Structure-Aware Approach
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
An integrated framework for de-identifying unstructured medical data
Data & Knowledge Engineering
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
Distribution based microdata anonymization
Proceedings of the VLDB Endowment
Encryption policies for regulating access to outsourced data
ACM Transactions on Database Systems (TODS)
Towards publishing recommendation data with predictive anonymization
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Keep a few: outsourcing data while maintaining confidentiality
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
Towards bipartite graph data management
CloudDB '10 Proceedings of the second international workshop on Cloud data management
Fragments and loose associations: respecting privacy in data publishing
Proceedings of the VLDB Endowment
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
ACM Transactions on Database Systems (TODS)
Neighborhood-privacy protected shortest distance computing in cloud
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
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
Protecting sensitive relationships against inference attacks in social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Semi-Edge anonymity: graph publication when the protection algorithm is available
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Privacy preservation by disassociation
Proceedings of the VLDB Endowment
Injecting uncertainty in graphs for identity obfuscation
Proceedings of the VLDB Endowment
Delineating social network data anonymization via random edge perturbation
Proceedings of the 21st ACM international conference on Information and knowledge management
Privacy protection in social networks using l-diversity
ICICS'12 Proceedings of the 14th international conference on Information and Communications Security
Outsourcing shortest distance computing with privacy protection
The VLDB Journal — The International Journal on Very Large Data Bases
Extending loose associations to multiple fragments
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
Graph publication when the protection algorithm is available
Data & Knowledge Engineering
Structure-aware graph anonymization
Web Intelligence and Agent Systems
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Private data often comes in the form of associations between entities, such as customers and products bought from a pharmacy, which are naturally represented in the form of a large, sparse bipartite graph. As with tabular data, it is desirable to be able to publish anonymized versions of such data, to allow others to perform ad hoc analysis of aggregate graph properties. However, existing tabular anonymization techniques do not give useful or meaningful results when applied to graphs: small changes or masking of the edge structure can radically change aggregate graph properties. We introduce a new family of anonymizations, for bipartite graph data, called (k, l)-groupings. These groupings preserve the underlying graph structure perfectly, and instead anonymize the mapping from entities to nodes of the graph. We identify a class of "safe" (k, l)-groupings that have provable guarantees to resist a variety of attacks, and show how to find such safe groupings. We perform experiments on real bipartite graph data to study the utility of the anonymized version, and the impact of publishing alternate groupings of the same graph data. Our experiments demonstrate that (k, l)-groupings offer strong tradeoffs between privacy and utility.