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 analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.

As they compete for developers, mobile app ecosystems have been exposing a growing number of APIs through their software development kits. Many of these APIs involve accessing sensitive functionality and/or user data and require approval by users. Android for instance allows developers to select from over 130 possible permissions. Expecting users to review and possibly adjust settings related to these permissions has proven unrealistic. In this paper, we report on the results of a study analyzing people's privacy preferences when it comes to granting permissions to different mobile apps. Our results suggest that, while people's mobile app privacy preferences are diverse, a relatively small number of profiles can be identified that offer the promise of significantly simplifying the decisions mobile users have to make. Specifically, our results are based on the analysis of settings of 4.8 million smartphone users of a mobile security and privacy platform. The platform relies on a rooted version of Android where users are allowed to choose between "granting", "denying" or "requesting to be dynamically prompted" when it comes to granting 12 different Android permissions to mobile apps they have downloaded.

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 Wiscom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users' reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users' major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.

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 app both use one's location information, but people would likely view the map's use of that data as more legitimate than the game. Our work introduces a new model for privacy, namely privacy as expectations. We report on the results of using crowdsourcing to capture users' expectations of what sensitive resources mobile apps use. We also report on a new privacy summary interface that prioritizes and highlights places where mobile apps break people's expectations. We conclude with a discussion of implications for employing crowdsourcing as a privacy evaluation technique.

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 it is needed. This approach allows an end-user to make use of location-enhanced content while only revealing to third-party content providers a large geographic region rather than a precise location. In this paper, we present an analysis that examines tradeoffs in terms of storage, bandwidth, and freshness of data. We then discuss the design and implementation of an Android service embodying these ideas. Finally, we provide two evaluations of Caché. One measures the performance of our approach with respect to privacy and mobile content availability using real-world mobility traces. The other focuses on our experiences using Caché to enhance user privacy in three open source Android applications.

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 first step, we analyze data from a two-week study involving 26 participants in two different cities, focusing on how people refer to places in location sharing. We derive a taxonomy of different place naming methods, and show that factors such as a person's perceived familiarity with a place and the entropy of that place (i.e. the variety of people who visit it) strongly influence the way people refer to it when interacting with others. We also present a machine learning model for predicting how people name places. Using our data, this model is able to predict the place naming method people choose with an average accuracy higher than 85%.

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 what we refer to as purpose-driven location sharing. We explore the differences between these two types of sharing by conducting a comparative two-week study with nine participants. We found significant differences in terms of users' decisions about what location information to share, their privacy concerns, and how privacy-preserving their disclosures were. Based on these results, we provide design implications for future LSAs.

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 Energy Efficient Mobile Sensing System (EEMSS). EEMSS uses hierarchical sensor management strategy to recognize user states as well as to detect state transitions. By powering only a minimum set of sensors and using appropriate sensor duty cycles EEMSS significantly improves device battery life. We present the design, implementation, and evaluation of EEMSS that automatically recognizes a set of users' daily activities in real time using sensors on an off-the-shelf high-end smart phone. Evaluation of EEMSS with 10 users over one week shows that our approach increases the device battery life by more than 75% while maintaining both high accuracy and low latency in identifying transitions between end-user activities.

Having an ID being unique within the application domain for each sensor node is the basic assumption in many WSN applications. In traditional assignments, fixed-length ID schemes, which are severely limited by scalability, flexibility and energy efficiency, are adopted. To solve these problems, we propose a novel variable-length ID scheme that allows different nodes to have different ID lengths. To realize this idea, we propose a fully localized ID assignment algorithm called emphself-organized ID assignment (SIDA). SIDA presents great potential on scalability, flexibility and energy efficiency by enabling online ID assignment and various optimizations according to different communication paradigms. Analytical and simulation results show that SIDA has a comparable static ID length while the accumulated communication overhead for multi-hop transmissions can be saved by 20%; the control overhead of the new deployment assignment can be reduced to about 30% compared with fixed-length schemes.