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PERCOM
2016
ACM

A machine-learning based approach to privacy-aware information-sharing in mobile social networks

4 years 3 months ago
A machine-learning based approach to privacy-aware information-sharing in mobile social networks
Contextual information about users is increasingly shared on mobile social networks. Examples of such information include users’ locations, events, activities, and the co-presence of others in proximity. When disclosing personal information, users take into account several factors to balance privacy, utility and convenience – they want to share the “right” amount and type of information at each time, thus revealing a selective sharing behavior depending on the context, with a minimum amount of user interaction. In this article, we present SPISM, a novel information-sharing system that decides (semi-)automatically, based on personal and contextual features, whether to share information with others and at what granularity, whenever it is requested. SPISM makes use of (active) machine-learning techniques, including cost-sensitive multi-class classifiers based on support vector machines. SPISM provides both ease of use and privacy features: It adapts to each user’s behavior and...
Igor Bilogrevic, Kévin Huguenin, Berker Agi
Added 08 Apr 2016
Updated 08 Apr 2016
Type Journal
Year 2016
Where PERCOM
Authors Igor Bilogrevic, Kévin Huguenin, Berker Agir, Murtuza Jadliwala, Maria Gazaki, Jean-Pierre Hubaux
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