# Securing Immersive 360 Video Streams through Attribute-Based Selective Encryption

arXiv: https://arxiv.org/abs/2505.04466 · HTML: https://mlsanity.com/paper/2505.04466/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2025.

## Built on by (most cited first, 2 of 2)

- [Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications](https://mlsanity.com/paper/2512.15823/) (2025)
- [Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds](https://mlsanity.com/paper/2609.02568/) (2026)

## Abstract (arXiv)

Delivering high-quality, secure 360deg 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 360deg 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).