关键词: 手机隐私, 权限, 意图, 隐私评分, 机器学习

Abstract:

Mobile Apps frequently request access to sensitive information without users' knowledge. Whether the sensitive information should be granted is related to the purpose of permission use. This paper proposes a privacy rating model to assess the privacy behavior of Android Apps based on the purpose of permission use and users' expectation. Based on 16651 crowdsourcing data of 421 users for the triple <app, permission, purpose>, this paper trains a privacy rating model based on machine-learning techniques. Then this paper uses static analysis to infer the purpose of permission use in the App, and grades the privacy by using privacy rating model. The experiment results show that the privacy rating model can achieve 80.7% accuracy. By applying the privacy rating model to 11931 Apps from Google Play, the results show that around 8% of Apps have serious privacy risks.

Key words: mobile privacy, permission, purpose, privacy score, machine learning