Bilogrevic, IgorHuguenin, KévinAgir, BerkerJadliwala, MurtuzaGazaki, MariaHubaux, Jean-Pierre2015-01-252015-01-252015-01-25201610.1016/j.pmcj.2015.01.006https://infoscience.epfl.ch/handle/20.500.14299/110515WOS:000370771200007Contextual 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.Information-sharingDecision-makingMachine learningUser studyPrivacytext::journal::journal article::research article