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Recent studies have shown that many users struggle to properly manage selective sharing of the diverse information artefacts they deposit in social software tools. Most tools define privacy based on the ‘network of friends’ model, in which all ‘friends’ are created equal and all relationships are reciprocal. This model fails to support the privacy expectations that non-technical users bring from their real-life experiences, such as enabling different degrees of intimacy within one’s network and providing flexible, natural means of managing the volatile social relationships that social software systems confront. Furthermore, the model suffers from lack of empirical grounding and systematic evaluation. This paper presents a framework for building privacy management mechanisms for social software systems that is intuitive and easy to use for the average, non-technical user population of these systems. The framework is based on a grounded theory study of users’ information sharing behaviour in a social software tool. Results inform the design of OpnTag, a social software prototype that facilitates personal and social information management and sharing. Preliminary empirical data suggest that our proposed privacy framework is flexible enough to meet users’ varying information sharing needs in different contexts while maintaining adequate support for usability.

Abstract: Social network services have become widely used as an important tool to share rich information; in such networks, making new friends is the most basic functionality to enable users to take advantage of their social networks. In this paper we look into personal photos as an additional source for social network analysis and analyze the potential of people tags in the photos for friend recommendations. We also propose a new compact data structure, collectively called Face Co-Occurrence Networks (FCON), which stores crucial and quantitative information about people’s appearance in photos. We discover strong associative relationships among people and recommend reliable social friends by utilizing FCON. Experimental results demonstrate the effectiveness and efficiency of our method for recommending friends in social network services. [Copyright &y& Elsevier]