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

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Bilogrevic, Igor
Huguenin, Kevin
Agir, Berker
Jadliwala, Murtuza Shabbir
Gazaki, Maria
Hubaux, Jean-Pierre

Bilogrevic, Igor; Huguenin, Kevin; Agir, Berker; Jadliwala, Murtuza Shabbir; Gazaki, Maria; E. 2016. A machine-learning based approach to privacy-aware information-sharing in mobile social networks. Pervasive and Mobile Computing, vol. 25:pp 125–142


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 predicts the level of detail for each sharing decision. Based on a personalized survey about information sharing, which involves 70 participants, our results provide insight into the most influential features behind a sharing decision, the reasons users share different types of information and their confidence in such decisions. We show that SPISM outperforms other kinds of policies; it achieves a median proportion of correct sharing decisions of 72% (after only 40 manual decisions). We also show that SPISM can be optimized to gracefully balance utility and privacy, but at the cost of a slight decrease in accuracy. Finally, we assess the potential of a one-size-fits-all version of SPISM.

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