PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy
Xianzhi Zhang, Yipeng Zhou, Di Wu, Quan Z. Sheng, Miao Hu, Linchang Xiao

TL;DR
This paper introduces PPVF, a privacy-preserving online video fetching framework that uses trusted edge devices and correlated differential privacy to protect user request privacy while maintaining high caching efficiency.
Contribution
The paper proposes a novel PPVF framework with an online privacy scheduler, noisy request generator, and federated learning-based utility predictor, enhancing privacy and efficiency in video streaming.
Findings
PPVF effectively protects user request privacy.
PPVF maintains high video caching performance.
Experimental results on Tencent data validate effectiveness.
Abstract
Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are automatically seized by video content providers, potentially leaking users' privacy. Unfortunately, current protection methods are not well-suited to preserving user request privacy from content providers while maintaining high-quality online video services. To tackle this challenge, we introduce a novel Privacy-Preserving Video Fetching (PPVF) framework, which utilizes trusted edge devices to pre-fetch and cache videos, ensuring the privacy of users' requests while optimizing the efficiency of edge caching. More specifically, we design PPVF with three core components: (1) \textit{Online privacy budget scheduler}, which employs a theoretically…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Sexuality, Behavior, and Technology · Privacy, Security, and Data Protection
