Date of Award
Master of Science (MS)
Computer Science and Engineering
While Information and Communication Technology (ICT) trends are moving towards the Internet of Things (IoT), mobile applications are becoming more and more popular. Mostly due to their pervasiveness and the level of interaction with the users, along with the great number of advantages, the mobile applications bring up a great number of privacy related issues as well. These platforms can gather our very sensitive private data by only granting them a list of permissions during the installation process. Additionally, most of the users can find it difficult, or even useless, to analyze system permissions. Thus, their guess of app’s safety mostly relies on the features like rating and popularity, rather than in understanding context of listed permissions.
In this work, we investigate the relationship between the features collected from Android Market API 23 and app’s privacy violation. These features include Popularity, Total Number of Permissions, Number of Dangerous Permissions, Rating and Package Size. To show the influence of each feature we use linear regression and Pearson R statistics.
The conducted research can contribute to the classification of mobile applications concerning the threat on user’s privacy.
Gashi, Erza, "Permission-based Privacy Analysis for Android Applications" (2018). Theses and Dissertations. 1.