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      How Dangerous Are Your Smartphones? App Usage Recommendation with Privacy Preserving

      , , , ,
      Mobile Information Systems
      Hindawi Limited

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          Abstract

          With the rapid proliferation of mobile devices, explosive mobile applications (apps) are developed in the past few years. However, the functions of mobile apps are varied and the designs of them are not well understood by end users, especially the activities and functions related to user privacy. Therefore, understanding how much danger of mobile apps with respect to privacy violation to mobile users is becomes a critical issue when people use mobile devices. In this paper, we evaluate the mobile app privacy violation of mobile users by computing the danger coefficient. In order to help people reduce the privacy leakage, we combine both the user preference to mobile apps and the privacy risk of apps and propose a mobile app usage recommendation method named AppURank to recommend the secure apps with the same function as the “dangerous” one for people use. The evaluation results show that our recommendation can reduce the privacy leakage by 50%.

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          Mining Mobile User Preferences for Personalized Context-Aware Recommendation

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            Towards Personalized Context-Aware Recommendation by Mining Context Logs through Topic Models

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              Handbook of The Poisson Distribution

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                Author and article information

                Journal
                Mobile Information Systems
                Mobile Information Systems
                Hindawi Limited
                1574-017X
                1875-905X
                2016
                2016
                : 2016
                :
                : 1-10
                Article
                10.1155/2016/6804379
                57643b8a-fa32-4192-9c56-80e87b616e56
                © 2016

                http://creativecommons.org/licenses/by/4.0/

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