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...

• Type: Journal
• Year: 2016
• Where: PERCOM
• Authors: Igor Bilogrevic, Kévin Huguenin, Berker Agir, Murtuza Jadliwala, Maria Gazaki, Jean-Pierre Hubaux
• Keywords: Computer Networks, PERCOM 2016