Smartphone usage is tightly coupled with the use of apps that can be either free or paid. Numerous studies have investigated the tracking libraries associated with free apps. Only a limited number of these have focused on paid apps. As expected, these investigations indicate that tracking is happening to a lesser extent in paid apps, yet there is no conclusive evidence. This paper provides the first large-scale study of paid apps. We analyse top paid apps obtained from four different countries: Australia, Brazil, Germany, and US, and quantify the level of tracking taking place in paid apps in comparison to free apps. Our analysis shows that 60% of the paid apps are connected to trackers that collect personal information compared to 85%-95% in free apps. We further show that approximately 20% of the paid apps are connected to more than three trackers. With tracking being pervasive in both free and paid apps, we then quantify the aggregated privacy leakages associated with individual users. Using the data of user installed apps of over 300 smartphone users, we show that 50% of the users are exposed to more than 25 trackers which can result in significant leakages of privacy.

With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain unknown drones. While there are several methods proposed for detecting the presence of a drone, they have limitations when it comes to low visibility, limited access, or hostile environments. In this paper, we propose DronePrint that uses drone acoustic signatures to detect the presence of a drone and identify the make and the model of the drone. We address the shortage of drone acoustic data by relying on audio components of online videos. In drone detection, we achieved 96% accuracy in a closed-set scenario, and 86% accuracy in a more challenging open-set scenario. Our proposed method of cascaded drone identification, where a drone is identified for its 'make' followed by the 'model' of the drone achieves 90% overall accuracy. In this work, we cover 13 commonly used commercial and consumer drone models, which is to the best of understanding is the most comprehensive such study to date. Finally, we demonstrate the robustness of DronePrint to drone hardware modifications, Doppler effect, varying SNR conditions, and in realistic open-set acoustic scenes.

No abstract