PrivaTube: Privacy-Preserving Edge-Assisted Video Streaming

Abstract

Video on Demand (VoD) streaming is the largest source of Internet traffic. Efficient and scalable VoD requires Content Delivery Networks (CDNs) whose cost are prohibitive for many providers. An alternative is to cache and serve video content using end-users devices. Direct connections between these devices complement the resources of core VoD servers with an edge-assisted collaborative CDN. VoD access histories can reveal critical personal information, and centralized VoD solutions are notorious for exploiting personal data. Hiding the interests of users from servers and edge-assisting devices is necessary for a new generation of privacy-preserving VoD services. We introduce PrivaTube, a scalable and cost-effective VoD solution. PrivaTube aggregates video content from multiple servers and edge peers to offer a high Quality of Experience (QoE) for its users. It enables privacy preservation at all levels of the content distribution process. It leverages Trusted Execution Environments (TEEs) at servers and clients, and obfuscates access patterns using fake requests that reduce the risk of personal information leaks. Fake requests are further leveraged to implement proactive provisioning and improve QoE. Our evaluation of a complete prototype shows that PrivaTube reduces the load on servers and increases QoE while providing strong privacy guarantees.