Predicting Users Mobile App Privacy Preferences

HTML  XML Download Download as PDF (Size: 610KB)  PP. 147-156  
DOI: 10.4236/jcc.2019.710014    452 Downloads   1,334 Views  Citations

ABSTRACT

Home users are using a wide and increasing range of different technologies, devices, platforms, applications and services every day. In parallel, home users are also installing and using an enormous number of apps, which collect and share a large amount of data. Users are also often unaware of what information apps collect about them, which is really valuable and sensitive for them. Therefore, users are becoming increasingly concerned about their personal information that is stored in these apps. While most mobile operating systems such as Android and iOS provide some privacy safeguards for users, it is unrealistic to manage and control a large volume of data. Accordingly, there is a need for a new technique, which has the ability to predict many of a user’s mobile app privacy preferences. A major contribution of this work is to utilise different machine learning techniques for assigning users to the privacy profiles that most closely capture their privacy preferences. Applying privacy profiles as default settings for initial interfaces could significantly reduce the burden and frustration of the user. The result shows that it’s possible to reduce the user’s burden from 46 to 10 questions by achieving 86% accuracy, which indicates that it’s possible to predict many of a user’s mobile app privacy preferences by asking the user a small number of questions.

Share and Cite:

Alshehri, A. and Alotaibi, F. (2019) Predicting Users Mobile App Privacy Preferences. Journal of Computer and Communications, 7, 147-156. doi: 10.4236/jcc.2019.710014.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.