Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Demographic prediction based on user's browsing behavior
Proceedings of the 16th international conference on World Wide Web
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
NOYB: privacy in online social networks
Proceedings of the first workshop on Online social networks
FlyByNight: mitigating the privacy risks of social networking
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Link privacy in social networks
Proceedings of the 17th ACM conference on Information and knowledge management
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
FaceCloak: An Architecture for User Privacy on Social Networking Sites
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Cautious Collective Classification
The Journal of Machine Learning Research
Do online social network friends still threaten my privacy?
Proceedings of the third ACM conference on Data and application security and privacy
Music similarity and retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Exploiting innocuous activity for correlating users across sites
Proceedings of the 22nd international conference on World Wide Web
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Abstract Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user profiling in social networks is described, and its performance is reported and discussed. Secondly, the experiments --conducted on information usually considered sensitive-- reveal that by just publicizing one's contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined.