Proceedings of the 16th international conference on World Wide Web
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
Collective privacy management in social networks
Proceedings of the 18th international conference on World wide web
Proceedings of the 18th international conference on World wide web
Inferring private information using social network data
Proceedings of the 18th international conference on World wide web
On Link Privacy in Randomizing Social Networks
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A brief survey on anonymization techniques for privacy preserving publishing of social network data
ACM SIGKDD Explorations Newsletter
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Relationship privacy: output perturbation for queries with joins
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Accurate Estimation of the Degree Distribution of Private Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A Framework for Computing the Privacy Scores of Users in Online Social Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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
Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
Privacy in dynamic social networks
Proceedings of the 19th international conference on World wide web
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
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Prediction promotes privacy in dynamic social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
A firm foundation for private data analysis
Communications of the ACM
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
Resisting structural re-identification in anonymized social networks
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy Violations Using Microtargeted Ads: A Case Study
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
A workflow for differentially-private graph synthesis
Proceedings of the 2012 ACM workshop on Workshop on online social networks
STK-anonymity: k-anonymity of social networks containing both structural and textual information
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
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The proliferation of information networks, as a means of sharing information, has raised privacy concerns for enterprises who manage such networks and for individual users that participate in such networks. For enterprises, the main challenge is to satisfy two competing goals: releasing network data for useful data analysis and also preserving the identities or sensitive relationships of the individuals participating in the network. Individual users, on the other hand, require personalized methods that increase their awareness of the visibility of their private information. This tutorial provides a systematic survey of the problems and state-of-the-art methods related to both enterprise and personalized privacy in information networks. The tutorial discusses privacy threats, privacy attacks, and privacy-preserving mechanisms tailored specifically to network data.