Efficient yet Robust Privacy Preservation for MPEG-DASH-Based Video Streaming

Abstract

MPEG-DASH is a video streaming standard that outlines protocols for sending audio and video content from a server to a client over HTTP. However, it creates an opportunity for an adversary to invade users' privacy. While a user is watching a video, information is leaked in the form of meta-data, the size of data and the time the server sent the data to the user. After a fingerprint of this data is created, the adversary can use this to identify whether a target user is watching the corresponding video. Only one defense strategy has been proposed to deal with this problem: differential privacy that adds sufficient noise in order to muddle the attacks. However, that strategy still suffers from the trade-off between privacy and efficiency. This paper proposes a novel defense strategy against the attacks with rigorous privacy and performance goals creating a private, scalable solution. Our algorithm, "No Data are Alone" (NDA), is highly efficient. The experimental results show that our scheme is more than two times efficient in terms of excess downloaded video (represented as waste) compared to the most efficient differential privacy-based scheme. Additionally, no classifier can achieve an accuracy above 7.07% against videos obfuscated with our scheme.