Modern mobile operating systems implement an ask-on-first-use policy to regulate applications’ access to private user data: the user is prompted to allow or deny access to a sensitive resource the first time an app attempts to use it. Prior research shows that this model may not adequately capture user privacy preferences because subsequent requests may occur under varying contexts. To address this shortcoming, LERSSE’s PhD student Primal Wijesekera led a collaboration project with Dr. Egelman‘s Berkeley Laboratory for Usable and Experimental Security (BLUES) to implement a novel privacy management system in Android, in which contextual signals are used to build a classifier that predicts user privacy preferences under various scenarios. Continue reading
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In my undergraduate course on security, we are holding a mini-conference on December 4, where each team of 3-4 students will present their term project. Project topics are diverse and practical. The mini-conference is open to public. See its schedule for location information and presentation times. The projects will be evaluated by the representatives of the high-tech industry.