Privis: Towards Content-Aware Secure Volumetric Video Delivery

TL;DR

Privis introduces a content-aware, saliency-guided framework for secure, low-latency delivery of volumetric video in XR, addressing the unique privacy and latency challenges of 3D immersive content.

Contribution

It proposes a novel transport-layer security architecture tailored for volumetric video, incorporating adaptive encryption, saliency-based partitioning, and traffic shaping for real-time XR applications.

Findings

Prototype implementation demonstrates feasibility.

Initial latency measurements show acceptable performance.

Framework balances confidentiality with low latency.

Abstract

Volumetric video has emerged as a key paradigm in eXtended Reality (XR) and immersive multimedia because it enables highly interactive, spatially consistent 3D experiences. However, the transport-layer security for such 3D content remains largely unaddressed. Existing volumetric streaming pipelines inherit uniform encryption schemes from 2D video, overlooking the heterogeneous privacy sensitivity of different geometry and the strict motion-to-photon latency constraints of real-time XR. We take an initial step toward content-aware secure volumetric video delivery by introducing Privis, a saliency-guided transport framework that (i) partitions volumetric assets into independent units, (ii) applies lightweight authenticated encryption with adaptive key rotation, and (iii) employs selective traffic shaping to balance confidentiality and low latency. Privis specifies a generalized transport-layer security architecture for volumetric media, defining core abstractions and adaptive protection mechanisms. We further explore a prototype implementation and present initial latency measurements to illustrate feasibility and design tradeoffs, providing early empirical guidance toward future work on real-time, saliency-conditioned secure delivery.