Semi-Supervised Policy Recommendation for Online Social Networks

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Provided by: University of North Alabama
Topic: Security
Format: PDF
Fine grain policy settings in social network sites are becoming a very important requirement for managing user's privacy. Incorrect privacy policy settings can easily lead to leaks in private and personal information. At the same time, being too restrictive would reduce the benefits of online social networks. This is further complicated with the growing adoption of social networks and with the rapid growth in information uploading and sharing. The problem of facilitating policy settings has attracted numerous access control, and human computer interaction researchers. The solutions proposed range from usable interfaces for policy settings to automated policy settings. The authors propose a fine grained policy recommendation system that is based on an iterative semi-supervised learning approach that uses the social graph propagation properties.
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