Clique is hard to approximate within n1-
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Involuntary Information Leakage in Social Network Services
IWSEC '08 Proceedings of the 3rd International Workshop on Security: Advances in Information and Computer Security
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Computational Complexity: A Modern Approach
Computational Complexity: A Modern Approach
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
New Approach to Quantification of Privacy on Social Network Sites
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Talking in circles: selective sharing in google+
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Maximizing circle of trust in online social networks
Proceedings of the 23rd ACM conference on Hypertext and social media
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With a rapid expansion of online social networks (OSNs), millions of users are tweeting and sharing their personal status daily without being aware of where that information eventually travels to. Likewise, with a huge magnitude of data available on OSNs, it poses a substantial challenge to track how a piece of information leaks to specific targets. In this paper, we study the problem of smartly sharing information to control the propagation of sensitive information in OSNs. In particular, we formulate and investigate the Maximum Circle of Trust problem of which we seek to construct a circle of trust on the fly so that OSN users can safely share their information knowing that it will not be propagated to their unwanted targets (whom they are not willing to share with). Since most of messages in OSNs are propagated within 2 to 5 hops, we first investigate this problem under 2-hop information propagation by showing the hardness of obtaining an optimal solution, along with an algorithm with proven performance guarantee. In a general case where information can be propagated more than two hops, the problem is #P-hard i.e. the problem cannot be solved in a polynomial time. Thus we propose a novel greedy algorithm, hybridizing the handy but costly sampling method with a novel cut-based estimation. The quality of the hybrid algorithm is comparable to that of the sampling method while taking only a tiny fraction of the time. We have validated the effectiveness of our solutions in many real-world traces. Such an extensive experiment also highlights several important observations on information leakage which help to sharpen the security of OSNs in the future.