Video Aficionado: We Know What You Are Watching

Users enjoy the convenience of watching videos on smart devices. However, video watching records can be exposed without users&#x2019; knowledge and be exploited to infer private information. In this paper, we design and implement a new side-channel attack system, named <italic>video aficionado</italic>, which can identify video watching information without violating any access control policies on Android. Our system only needs to collect power consumption data of a video playing app, which does not require explicit user permission. The collected data is sent to a remote server, where noise is cleaned and identified by a multi-layer perceptron (MLP) trained classifier. We evaluate our proposed system through a set of carefully designed experiments. Experimental results demonstrate that our system can make an identification with 74.5 percent accuracy on average for each 20-second power measurement segment out of 3918 segments collected from 20 videos. To the best of our knowledge, video aficionado is the first real-time power consumption-based video identification system on smart devices.