arXiv:2408.14735v1 Announce Type: new
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 guaranteed online algorithm to select non-requested videos as candidates with assigned privacy budgets. Alternative videos are chosen by an online algorithm that is theoretically guaranteed to consider both video utilities and available privacy budgets. (2) textit{Noisy video request generator}, which generates redundant video requests (in addition to original ones) utilizing correlated differential privacy to obfuscate request privacy. (3) textit{Online video utility predictor}, which leverages federated learning to collaboratively evaluate video utility in an online fashion, aiding in video selection in (1) and noise generation in (2). Finally, we conduct extensive experiments using real-world video request traces from Tencent Video. The results demonstrate that PPVF effectively safeguards user request privacy while upholding high video caching performance.
In this article, the authors discuss the privacy challenges associated with online video streaming and propose a novel framework called Privacy-Preserving Video Fetching (PPVF) to address these challenges. They highlight the importance of preserving user request privacy while ensuring the efficiency of edge caching in video content delivery.
The multi-disciplinary nature of this concept becomes evident as the authors discuss the three core components of the PPVF framework. Firstly, they introduce the “Online privacy budget scheduler” which utilizes an online algorithm to select non-requested videos as candidates based on assigned privacy budgets. This involves considering both video utilities and available privacy budgets, demonstrating the incorporation of online algorithms and optimization techniques.
Secondly, the “Noisy video request generator” is introduced, which generates redundant video requests utilizing correlated differential privacy. This technique aims to obfuscate the original video requests and enhance user request privacy. Differential privacy is a concept from the field of privacy-preserving data mining and by incorporating it into the video streaming context, the authors showcase the interdisciplinary nature of the PPVF framework.
The third core component is the “Online video utility predictor” which leverages federated learning to evaluate video utility in an online fashion. Federated learning is a technique from the field of machine learning where the model is trained on decentralized data, preserving privacy. By using federated learning in the context of video utility prediction, the authors demonstrate the integration of machine learning techniques into the PPVF framework.
Overall, this article is related to the wider field of multimedia information systems as it delves into the challenges of online video streaming and proposes a framework to address privacy concerns while optimizing video caching performance. The concepts of artificial reality, augmented reality, and virtual realities are not directly discussed in this specific article, but they are all areas where online video streaming plays a significant role. Privacy-preserving frameworks like PPVF can contribute to maintaining privacy and security in these immersive multimedia environments.