Predicting Privacy Decisions in Mobile Applications and Raising Users’ Privacy Awareness

Abstract

Smartphones and mobile applications are now an integral part of our daily lives. Managing mobile privacy is a challenging task, that overwhelms users with numerous and complex privacy decisions related to permission settings. Many users do not have sufficient knowledge and understanding of how applications use their personal data, and others do not spend enough time configuring these settings. Various approaches to assist users by automating their decisions have been proposed in the literature. This paper presents a literature review of the current state of knowledge in the area of permission settings and the solutions proposed by different researchers, focusing on the use of machine learning techniques. Machine learning can address the challenges of mobile privacy management by learning users' preferences and predicting their decisions based on a relatively small number of factors. We then describe our future research plans to reduce the user's burden in configuring application permissions, to increase their awareness, and to protect their privacy.