The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Communications of the ACM
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
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy in dynamic social networks
Proceedings of the 19th international conference on World wide web
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Utility-driven anonymization in data publishing
Proceedings of the 20th ACM international conference on Information and knowledge management
Anonymizing set-valued data by nonreciprocal recoding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Sensitive label privacy protection on social network data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Delineating social network data anonymization via random edge perturbation
Proceedings of the 21st ACM international conference on Information and knowledge management
How to hack into Facebook without being a hacker
Proceedings of the 22nd international conference on World Wide Web companion
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The proliferation of online social networks has created intense interest in studying their nature and revealing information of interest to the end user. At the same time, such revelation raises privacy concerns. Existing research addresses this problem following an approach popular in the database community: a model of data privacy is defined, and the data is rendered in a form that satisfies the constraints of that model while aiming to maximize some utility measure. Still, these is no consensus on a clear and quantifiable utility measure over graph data. In this paper, we take a different approach: we define a utility guarantee, in terms of certain graph properties being preserved, that should be respected when releasing data, while otherwise distorting the graph to an extend desired for the sake of confidentiality. We propose a form of data release which builds on current practice in social network platforms: A user may want to see a subgraph of the network graph, in which that user as well as connections and affiliates participate. Such a snapshot should not allow malicious users to gain private information, yet provide useful information for benevolent users. We propose a mechanism to prepare data for user view under this setting. In an experimental study with real data, we demonstrate that our method preserves several properties of interest more successfully than methods that randomly distort the graph to an equal extent, while withstanding structural attacks proposed in the literature.