Gian Paolo Perrucci

Energy measurements campaign for positioning methods on State-of-the-Art smartphones

Mobile phones have been undergoing a breathtaking evolution over the last years, starting from simple devices with only voice services, nowadays they are referred as smartphones, powerful devices able to offer appealing services. One of the services that has recently become popular is referred as Location Based Services that use contextual and location information to enhance mobile user experience. As a basic assumption, this kind of services needs to be continuously aware of the phones location. The challenge emerges from the trade-off needed between the location precision and the energy consumption of the positioning methods, the most precise being also the energy-hungriest. This paper presents the results of an extensive energy measurements campaign that was conducted on different sensors and positioning methods on a smartphone. Results will help researchers and developers to design energy efficient and more accurate continuous location tracking algorithms to support Location Based Services.

Privacy Panel: Usable and Quantifiable Mobile Privacy

The ever increasing popularity of apps stems from their ability to provide highly customized services to the user. The flip side is that in order to provide such services, apps need access to very sensitive private information about the user. This leads to malicious apps that collect personal user information in the background and exploit it in various ways. Studies have shown that current app vetting processes which are mainly restricted to install time verification mechanisms are incapable of detecting and preventing such attacks. We argue that the missing fundamental aspect here is a comprehensive and usable mobile privacy solution, one that not only protects the users location information, but also other equally sensitive user data such as the users contacts and documents. A solution that is usable by the average user who does not understand or care about the low level technical details. To bridge this gap, we propose privacy metrics that quantify low-level app accesses in terms of privacy impact and transforms them to high-level user understandable ratings. We also provide the design and architecture of our Privacy Panel app that represents the computed ratings in a graphical user-friendly format and allows the user to define policies based on them. Finally, experimental results are given to validate the scalability of the proposed solution.