When privacy meets usability : unobtrusive privacy permission recommendation system for mobile apps based on crowdsourcing

摘要

People nowadays almost want everything at their fingertips, from business to entertainment, and meanwhile they do not want to leak their sensitive data. Strong information protection can be a competitive advantage, but preserving privacy is a real challenge when people use the mobile apps in the smartphone. If they are too lax with privacy preserving, important or sensitive information could be lost. If they are too tight with privacy, making users jump through endless hoops to access the data they need to get their work done, productivity can nosedive. Thus, striking a balance between privacy and usability in mobile applications can be difficult. Leveraging the privacy permission settings in mobile operating systems, our basic idea to address this issue is to provide proper recommendations about the settings so that the users can preserve their sensitive information and maintain the usability of apps. In this paper, we propose an unobtrusive recommendation system to implement this idea, which can crowdsource users’ privacy permission settings and generate the recommendations for them accordingly. Besides, our system allows users to provide feedback to revise the recommendations for getting better performance and adapting different scenarios. For the evaluation, we collected users’ preferences from 382 participants on Amazon Technical Turks and released our system to users in the real world for 10 days. According to the study, our system can make appropriate recommendations which can meet participants’ privacy expectation and mobile apps’ usability.