Browsing by Subject "smartphone"

Now showing 1 - 3 of 3
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    Determining Driver Phone Use by Exploiting Smartphone Integrated Sensors
    (IEEE, 2016-08) Wang, Yan; Chen, Yingying (Jennifer); Yang, Jie; Gruteser, Marco; Martin, Richard P.; Liu, Hongbo; Liu, Luyang; Karatas, Cagdas; Department of Engineering Technology, School of Engineering and Technology
    This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over 90 percent with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95 percent) with a lower false positive rate. In addition, we seek to exploit the electromagnetic field measurement inside a vehicle to complement vehicle dynamics for driver phone sensing under the scenarios when little vehicle dynamics is present, for example, driving straight on highways or standing at roadsides.
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    Implications of smartphone user privacy leakage from the advertiser’s perspective
    (Elsevier, 2019-02) Wang, Yan; Chen, Yingying; Ye, Fan; Liu, Hongbo; Yang, Jie; Computer Information and Graphics Technology, School of Engineering and Technology
    Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps’ behavior, the understanding of the consequences of such privacy losses remains limited. In this paper, we investigate how much an advertiser can infer about users’ social and community relationships. After one month’s user study involving about 190 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks’ user data. Finally, we present a real-time privacy leakage visualization tool that captures and displays the spatial–temporal characteristics of the leakages. The discoveries underscore the importance of early adoption of privacy protection mechanisms.
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    Towards Understanding the Advertiser’s Perspective of Smartphone User Privacy
    (IEEE, 2015-06) Wang, Yan; Chen, Yingying; Ye, Fan; Yang, Jie; Liu, Hongbo; Department of Computer & Information Science, School of Science
    Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps' behavior, the understanding about the consequences of such privacy losses remains limited. In this paper we investigate how much an advertiser can infer about users' social and community relationships by combining data from multiple applications and across many users. After one month's user study involving about 200 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks' user data. The discoveries underscore the importance of early adoption of privacy protection mechanisms.