The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Communications of the ACM
Proceedings of the 16th international conference on World Wide Web
What Anyone Can Know: The Privacy Risks of Social Networking Sites
IEEE Security and Privacy
Communications of the ACM
Characterizing privacy in online social networks
Proceedings of the first workshop on Online social networks
Imagined communities: awareness, information sharing, and privacy on the facebook
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
Mapping Social Networks into P2P Directory Service
SOCINFO '09 Proceedings of the 2009 International Workshop on Social Informatics
Privacy-enhanced public view for social graphs
Proceedings of the 2nd ACM workshop on Social web search and mining
Exploiting social networking sites for spam
Proceedings of the 17th ACM conference on Computer and communications security
Cheap and automated socio-technical attacks based on social networking sites
Proceedings of the 3rd ACM workshop on Artificial intelligence and security
unfriendly: multi-party privacy risks in social networks
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
Understanding the behavior of malicious applications in social networks
IEEE Network: The Magazine of Global Internetworking
Who's your best friend?: targeted privacy attacks In location-sharing social networks
Proceedings of the 13th international conference on Ubiquitous computing
Revisiting link privacy in social networks
Proceedings of the second ACM conference on Data and Application Security and Privacy
Proceedings of the Fifth Workshop on Social Network Systems
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The popular social networking website Facebook exposes a "public view" of user profiles to search engines which includes eight of the user's friendship links. We examine what interesting properties of the complete social graph can be inferred from this public view. In experiments on real social network data, we were able to accurately approximate the degree and centrality of nodes, compute small dominating sets, find short paths between users, and detect community structure. This work demonstrates that it is difficult to safely reveal limited information about a social network.