用属性加密+智能加密，让360度视频更安全高效。
Securing Immersive 360 Video Streams through Attribute-Based Selective Encryption
- 按视口大小动态加密关键区域帧，减少计算开销。
- 实验显示缓存命中率提升，计算负载降低，画质接近HTTPS。
- 适合对安全性和性能要求高的沉浸式视频系统开发者。
高质量、安全的360°视频流传输面临高码率和交互需求带来的挑战。传统基于HTTPS的方法在计算效率和可扩展性上存在局限。本文提出一种融合属性基加密（ABE）与选择性加密的新框架，专为分块360°视频流设计。通过在不同层级对帧进行选择性加密，降低计算开销并保障访问安全。进一步引入视口自适应加密机制，动态加密占据用户视场更大区域的图块帧，增强关键区域安全性，避免边缘区域冗余加密。我们在CloudLab测试平台上部署并评估该方法，对比传统HTTPS流。实验结果表明，基于ABE的模型显著降低中间缓存的计算负载，提升缓存命中率，且在视频多方法评估融合（VMAF）指标下维持与HTTPS相当的视觉质量。
原文摘要 · Abstract (English)
Delivering high-quality, secure 360° video content introduces unique challenges, primarily due to the high bitrates and interactive demands of immersive media. Traditional HTTPS-based methods, although widely used, face limitations in computational efficiency and scalability when securing these high-resolution streams. To address these issues, this paper proposes a novel framework integrating Attribute-Based Encryption (ABE) with selective encryption techniques tailored specifically for tiled 360° video streaming. Our approach employs selective encryption of frames at varying levels to reduce computational overhead while ensuring robust protection against unauthorized access. Moreover, we explore viewport-adaptive encryption, dynamically encrypting more frames within tiles occupying larger portions of the viewer's field of view. This targeted method significantly enhances security in critical viewing areas without unnecessary overhead in peripheral regions. We deploy and evaluate our proposed approach using the CloudLab testbed, comparing its performance against traditional HTTPS streaming. Experimental results demonstrate that our ABE-based model achieves reduced computational load on intermediate caches, improves cache hit rates, and maintains comparable visual quality to HTTPS, as assessed by Video Multimethod Assessment Fusion (VMAF).
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