Android user privacy preserving through crowdsourcing

In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary privacy breach. However, the majority of normal users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose DroidNet, an Android permission control framework based on crowdsourcing. At its core, DroidNet runs new apps under probation mode without granting their permission requests upfront. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise ranking algorithm using a transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our simulation and real user experimental results demonstrate that DroidNet provides accurate recommendations and cover the majority of app requests given a small coverage from a small set of initial experts.