Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
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
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Characterizing privacy in online social networks
Proceedings of the first workshop on Online social networks
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Link privacy in social networks
Proceedings of the 17th ACM conference on Information and knowledge management
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
Privacy in dynamic social networks
Proceedings of the 19th international conference on World wide web
Prediction promotes privacy in dynamic social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
A Socratic method for validation of measurement-based networking research
Computer Communications
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
Neighborhood-privacy protected shortest distance computing in cloud
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
Surrogate parenthood: protected and informative graphs
Proceedings of the VLDB Endowment
Privacy-aware spam detection in social bookmarking systems
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Sharing graphs using differentially private graph models
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
On the privacy and utility of anonymized social networks
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
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
Fine-grained access control of personal data
Proceedings of the 17th ACM symposium on Access Control Models and Technologies
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
Privacy preserving social network publication on bipartite graphs
WISTP'12 Proceedings of the 6th IFIP WG 11.2 international conference on Information Security Theory and Practice: security, privacy and trust in computing systems and ambient intelligent ecosystems
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
Privacy protection in social networks using l-diversity
ICICS'12 Proceedings of the 14th international conference on Information and Communications Security
STK-anonymity: k-anonymity of social networks containing both structural and textual information
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
Outsourcing shortest distance computing with privacy protection
The VLDB Journal — The International Journal on Very Large Data Bases
Sensitive edges protection in social networks
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Relationship-based information sharing in cloud-based decentralized social networks
Proceedings of the 4th ACM conference on Data and application security and privacy
Graph publication when the protection algorithm is available
Data & Knowledge Engineering
Structure-aware graph anonymization
Web Intelligence and Agent Systems
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The recent rise in popularity of social networks, such as Facebook and MySpace, has created large quantities of data about interactions within these networks. Such data contains many private details about individuals so anonymization is required prior to attempts to make the data more widely available for scientific research. Prior work has considered simple graph data to be anonymized by removing all non-graph information and adding or deleting some edges. Since social network data is richer in details about the users and their interactions, loss of details due to anonymization limits the possibility for analysis. We present a new set of techniques for anonymizing social network data based on grouping the entities into classes, and masking the mapping between entities and the nodes that represent them in the anonymized graph. Our techniques allow queries over the rich data to be evaluated with high accuracy while guaranteeing resilience to certain types of attack. To prevent inference of interactions, we rely on a critical "safety condition" when forming these classes. We demonstrate utility via empirical data from social networking settings. We give examples of complex queries that may be posed and show that they can be answered over the anonymized data efficiently and accurately.