Do online social network friends still threaten my privacy?
Proceedings of the third ACM conference on Data and application security and privacy
PROTOSS: A Run Time Tool for Detecting Privacy Violations in Online Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Account Reachability: A Measure of Privacy Risk for Exposure of a User's Multiple SNS Accounts
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Detecting and predicting privacy violations in online social networks
Distributed and Parallel Databases
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Several efforts have been made for more privacy aware Online Social Networks (OSNs) to protect personal data against various privacy threats. However, despite the relevance of these proposals, we believe there is still the lack of a conceptual model on top of which privacy tools have to be designed. Central to this model should be the concept of risk. Therefore, in this paper, we propose a risk measure for OSNs. The aim is to associate a risk level with social network users in order to provide other users with a measure of how much it might be risky, in terms of disclosure of private information, to have interactions with them. We compute risk levels based on similarity and benefit measures, by also taking into account the user risk attitudes. In particular, we adopt an active learning approach for risk estimation, where user risk attitude is learned from few required user interactions. The risk estimation process discussed in this paper has been developed into a Facebook application and tested on real data. The experiments show the effectiveness of our proposal.