Adaptive Information-Sharing for Privacy-Aware Mobile Social Networks

Abstract : Personal and contextual information are increasingly shared via mobile social networks. Users' locations, activities and their co-presence can be shared easily with online "friends", as their smartphones already access such information from embedded sensors and storage. Yet, people usually exhibit selective sharing behavior depending on contextual attributes, thus showing that privacy, utility, and usability are paramount to the success of such online services. In this paper, we present SPISM, a novel information-sharing system that decides (semi-)automatically whether to share information with others, whenever they request it, and at what granularity. Based on active machine learning and context, SPISM adapts to each user's behavior and it predicts the level of detail for each sharing decision, without revealing any personal information to a third-party. Based on a personalized survey about information sharing involving 70 participants, our results provide insight into the most influential features behind a sharing decision. Moreover, we investigate the reasons for the users' decisions and their confidence in them. We show that SPISM outperforms other kinds of global and individual policies, by achieving up to 90% of correct decisions.


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Contributor : Kévin Huguenin <>
Submitted on : Friday, July 5, 2013 - 7:00:10 AM
Last modification on : Thursday, May 14, 2015 - 1:56:34 PM

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Igor Bilogrevic, Kévin Huguenin, Berker Ağır, Jadliwala Murtuza, Jean-Pierre Hubaux. Adaptive Information-Sharing for Privacy-Aware Mobile Social Networks. 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), Sep 2013, Zurich, Switzerland. pp.657-666, 2013, <10.1145/2493432.2493510>. <hal-00827622>

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