Two of the defining elements of Social Networking Services are the social profile, containing information about the user, and the social graph, containing information about the connections between users. Social Networking Services are used to connect to known people as well as to discover new contacts. Current friend recommendation mechanisms typically utilize the social graph. In this paper, we argue that psychometrics, the field of measuring personality traits, can help make meaningful friend recommendations based on an extended social profile containing collected smartphone sensor data. This will support the development of highly distributed Social Networking Services without central knowledge of the social graph.

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Online Social Networks have become one of the main tools for interpersonal online communication. In the age of the smartphone, mobile user scenarios become more and more important for Online Social Networks. Smartphones enable location-based and context-aware services, but bring the increased risk of privacy violations -at least in centralized OSN architectures. Decentralized Online Social Network architectures are promising as they inherently offer better privacy and less dependence on a single service provider, but they bring new challenges regarding core features of Online Social Networks. In this paper, we introduce a three-tiered view of the social graph and propose a new architecture for decentralized Online Social Networking applications, supporting the three-tiered view and focusing on location-based and context-aware user scenarios.

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