White Mirror: Leaking Sensitive Information from Interactive Netflix Movies using Encrypted Traffic Analysis

Abstract

Privacy leaks from Netflix videos/movies are well researched. Current state-of-the-art works have been able to obtain coarse-grained information such as the genre and the title of videos by passive observation of encrypted traffic. However, leakage of fine-grained information from encrypted video traffic has not been studied so far. Such information can be used to build behavioral profiles of viewers. Recently, Netflix released the first mainstream interactive movie called Black Mirror: Bandersnatch . In this work, we use this movie as a case-study to develop techniques for revealing information from encrypted interactive video traffic. We show for the first time that information such as the choices made by viewers can be revealed based on the characteristics of encrypted control traffic exchanged with Netflix. To evaluate our proposed technique, we built the first interactive video traffic dataset of 100 viewers; which we will be releasing. Our technique was able to reveal the choices 96% of the time in the case of Black Mirror: Bandersnatch and they were also equally or more successful for all other interactive movies released by Netflix so far.