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Urban sensing, participatory sensing, and user activity recognition can provide rich contextual information for mobile applications such as social networking and location-based services. However, continuously capturing this contextual information on mobile devices consumes huge amount of energy. In this paper, we present a novel design framework for an(More)
With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review(More)
User review is a crucial component of open mobile app markets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose(More)
Smartphone security research has produced many useful tools to analyze the privacy-related behaviors of mobile apps. However, these automated tools cannot assess people's perceptions of whether a given action is legitimate, or how that action makes them feel with respect to privacy. For example, automated tools might detect that a blackjack game and a map(More)
We present the design, implementation, and evaluation of Caché, a system that offers location privacy for certain classes of location-based applications. The core idea in Caché is to periodically pre-fetch potentially useful location-enhanced content well in advance. Applications then retrieve content from a local cache on the mobile device when(More)
Most location sharing applications display people's locations on a map. However, people use a rich variety of terms to refer to their locations, such as "home," "Starbucks," or "the bus stop near my house." Our long-term goal is to create a system that can automatically generate appropriate place names based on real-time context and user preferences. As a(More)
In this paper, we investigate the feasibility of identifying a small set of privacy profiles as a way of helping users manage their mobile app privacy preferences. Our analysis does not limit itself to looking at permissions people feel comfortable granting to an app. Instead it relies on static code analysis to determine the purpose for which an app(More)
With the widespread adoption of smartphones, mobile applications have gained mainstream popularity. However, the potential privacy and security risks associated with using mobile apps are quite high, as smartphones become increasingly integrated with our lives, being able to access our email, social networking accounts, financial information, personal(More)
With the widespread adoption of smartphones, mobile applications have gained mainstream popularity. However, the potential privacy and security risks associated with using mobile apps are quite high, as smartphones become increasingly integrated with our lives, being able to access our email, social networking accounts, financial information, personal(More)
The popularity of micro-blogging has made general-purpose information sharing a pervasive phenomenon. This trend is now impacting location sharing applications (LSAs) such that users are sharing their location data with a much wider and more diverse audience. In this paper, we describe this as social-driven sharing, distinguishing it from past examples of(More)