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dc.contributor.authorBilogrevic, Igor
dc.contributor.authorHuguenin, Kevin
dc.contributor.authorAgir, Berker
dc.contributor.authorJadliwala, Murtuza Shabbir
dc.contributor.authorGazaki, Maria
dc.contributor.authorHubaux, Jean-Pierre
dc.identifier.citationBilogrevic, 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 125–142en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractContextual 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.en_US
dc.description.sponsorshipSwiss National Science Foundation with grant 200021-138089.en_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofseriesPervasive and Mobile Computing;v.25
dc.subjectMachine learningen_US
dc.subjectUser studyen_US
dc.titleA machine-learning based approach to privacy-aware information-sharing in mobile social networksen_US
dc.rights.holderCopyright © 2015 Elsevier B.V. All rights reserved.en_US

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