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Privis: Towards Content-Aware Secure Volumetric Video Delivery

Kaiyuan Hu, Hong Kang, Yili Jin, Junhua Liu, Chengming Hu, Haolun Wu, Xue Liu

TL;DR

The paper addresses secure delivery of volumetric video under tight motion-to-photon latency and diverse privacy needs. It introduces Privis, a saliency-guided transport framework that partitions content into cubes, applies adaptive, per-cube AEAD, and employs traffic shaping to mitigate leakage while meeting MTP latency. Contributions include a formal saliency model $s(c) = \alpha \Phi_{p}(c) + (1-\alpha) \Phi_{s}(c)$, a cube-level protection policy $P(s(c))$, and a transport-layer architecture compatible with existing codecs. Prototype results on the FSVVD dataset show Privis achieves substantial confidentiality gains with only modest latency overhead, validating the feasibility of saliency-conditioned secure volumetric streaming.

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.

Privis: Towards Content-Aware Secure Volumetric Video Delivery

TL;DR

The paper addresses secure delivery of volumetric video under tight motion-to-photon latency and diverse privacy needs. It introduces Privis, a saliency-guided transport framework that partitions content into cubes, applies adaptive, per-cube AEAD, and employs traffic shaping to mitigate leakage while meeting MTP latency. Contributions include a formal saliency model , a cube-level protection policy , and a transport-layer architecture compatible with existing codecs. Prototype results on the FSVVD dataset show Privis achieves substantial confidentiality gains with only modest latency overhead, validating the feasibility of saliency-conditioned secure volumetric streaming.

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.

Paper Structure

This paper contains 16 sections, 7 equations, 1 figure, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Volumetric Video Streaming
  4. Security and Privacy in Immersive Media
  5. Design of Privis Framework
  6. System Overview
  7. Content-Aware Grouping
  8. Adaptive Protection and Key Management
  9. Traffic-Aware Secure Transport
  10. Client-Side Verification and Rendering Admission
  11. Evaluation
  12. Experimental Setup
  13. Evaluation Configurations
  14. Latency Metrics
  15. Results
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Privis performs saliency-driven cube partitioning, adaptive encryption, and latency-aware transmission for volumetric video. High-saliency regions are prioritized and strongly protected, while low-saliency content uses lighter security to maintain real-time performance. Client-side verification enforces decrypt-before-render, ensuring integrity in immersive delivery.