Privacy-enhanced public view for social graphs
Proceedings of the 2nd ACM workshop on Social web search and mining
Inferring privacy policies for social networking services
Proceedings of the 2nd ACM workshop on Security and artificial intelligence
Drac: an architecture for anonymous low-volume communications
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
Abusing social networks for automated user profiling
RAID'10 Proceedings of the 13th international conference on Recent advances in intrusion detection
Understanding the behavior of malicious applications in social networks
IEEE Network: The Magazine of Global Internetworking
A3P: adaptive policy prediction for shared images over popular content sharing sites
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Multi agent system for historical information retrieval from online social networks
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Revisiting link privacy in social networks
Proceedings of the second ACM conference on Data and Application Security and Privacy
Online social network platforms: toward a model-backed security evaluation
Proceedings of the 1st Workshop on Privacy and Security in Online Social Media
Bridge analysis in a Social Internetworking Scenario
Information Sciences: an International Journal
Multi agent system approach for vulnerability analysis of online social network profiles over time
International Journal of Knowledge and Web Intelligence
Visualizing the relevance of social ties in user profile modeling
Web Intelligence and Agent Systems
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Preventing adversaries from compiling significant amounts of user data is a major challenge for social network operators. We examine the difficulty of collecting profile and graph information from the popular social networking website Facebook and report two major findings. First, we describe several novel ways in which data can be extracted by third parties. Second, we demonstrate the efficiency of these methods on crawled data. Our findings highlight how the current protection of personal data is inconsistent with user's expectations of privacy.