Session
Computer Science and Communication Engineering
Description
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 paper we investigate the relationship between the features collected from Android Market API 23 (such as Popularity, Total Number of Permissions, Number of Dangerous Permissions, Rating and Package Size) to app’s privacy violation. To show the influence of each feature we use linear regression and R squared statistics. The conducted research can contribute to the classification of mobile applications with regards to the threat on user’s privacy.
Keywords:
android, applications, permission, privacy
Session Chair
Kozeta Sevran
Session Co-Chair
Bertan Karahoda
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-437-60-8
First Page
61
Last Page
71
Location
Durres, Albania
Start Date
27-10-2017 3:00 PM
End Date
27-10-2017 4:30 PM
DOI
10.33107/ubt-ic.2017.88
Recommended Citation
Gashi, Erza and Tafa, Zhilbert, "Permission-based Privacy Analysis for Android Applications" (2017). UBT International Conference. 88.
https://knowledgecenter.ubt-uni.net/conference/2017/all-events/88
Included in
Permission-based Privacy Analysis for Android Applications
Durres, Albania
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 paper we investigate the relationship between the features collected from Android Market API 23 (such as Popularity, Total Number of Permissions, Number of Dangerous Permissions, Rating and Package Size) to app’s privacy violation. To show the influence of each feature we use linear regression and R squared statistics. The conducted research can contribute to the classification of mobile applications with regards to the threat on user’s privacy.