Android Users Privacy Awareness Survey

Having a share of over 80% of the smartphone market, Android has become an important mobile operating system that is used by billions of users on daily basis. With the widespread use of smartphones in general, and Android in specific, privacy concerns grow with that expansion in the user base. With the millions of applications being downloaded by users daily, it is becoming increasingly difficult to differentiate between the good and the bad in terms of security and privacy. In this paper, we present the results of a survey conducted among 4027 Android users worldwide. This survey was conducted to measure the awareness of Android users regarding their privacy. The study measures the users' interaction with the permissions required by different applications they install. The results of the survey show apparent weakness in the awareness of Android users regarding the privacy of their data.

[1]  Anitha Ramalingam,et al.  Malware Detection in Android files based on Multiple levels of Learning and Diverse Data Sources , 2015, WCI '15.

[2]  Ninghui Li,et al.  Android permissions: a perspective combining risks and benefits , 2012, SACMAT '12.

[3]  Win Zaw,et al.  Permission-Based Android Malware Detection , 2013 .

[4]  Ayumu Kubota,et al.  Kernel-based Behavior Analysis for Android Malware Detection , 2011, 2011 Seventh International Conference on Computational Intelligence and Security.

[5]  Roland H. C. Yap,et al.  Inferring the Detection Logic and Evaluating the Effectiveness of Android Anti-Virus Apps , 2016, CODASPY.

[6]  Yajin Zhou,et al.  RiskRanker: scalable and accurate zero-day android malware detection , 2012, MobiSys '12.

[7]  Yajin Zhou,et al.  Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.

[8]  Hahn-Ming Lee,et al.  DroidMat: Android Malware Detection through Manifest and API Calls Tracing , 2012, 2012 Seventh Asia Joint Conference on Information Security.

[9]  Jules White,et al.  Applying machine learning classifiers to dynamic Android malware detection at scale , 2013, 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC).

[10]  Latifur Khan,et al.  A Machine Learning Approach to Android Malware Detection , 2012, 2012 European Intelligence and Security Informatics Conference.