Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism

2026-04-22

AI Summary

This work addresses the secure rate–distortion–perception (RDP) trade-off under noisy and broadcast channels for applications such as neural image compression, where security, distortion, and perceptual quality must be jointly optimized. Leveraging a randomized distributed functional computation framework and integrating information-theoretic techniques—including stochastic binning, separation-based source and channel coding, side information exploitation, and broadcast channel capacity analysis—the study establishes the exact secure RDP region for noisy channels and provides a tight inner bound for broadcast channels. It further demonstrates that common randomness substantially reduces communication rates. The proposed approach is validated on binary and Gaussian models, achieving strong secrecy, low distortion, and high perceptual quality simultaneously, and attaining theoretical optimality under certain conditions.

Technology Category

• Machine Learning: Information Theory
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Fundamental rate-distortion-perception (RDP) trade-offs arise in applications requiring maintained perceptual quality of reconstructed data, such as neural image compression. When compressed data is transmitted over public communication channels, security risks emerge. We therefore study secure RDP under negligible information leakage over both noiseless channels and broadcast channels, BCs, with correlated noise components. For noiseless channels, the exact secure RDP region is characterized. For BCs, an inner bound is derived and shown to be tight for a class of more-capable BCs. Separate source-channel coding is further shown to be optimal for this exact secure RDP region with unlimited common randomness available. Moreover, when both encoder and decoder have access to side information correlated with the source and the channel is noiseless, the exact RDP region is established. If only the decoder has correlated side information in the noiseless setting, an inner bound is derived along with a special case where the region is exact. Binary and Gaussian examples demonstrate that common randomness can significantly reduce the communication rate in secure RDP settings, unlike in standard rate-distortion settings. Thus, our results illustrate that random binning-based coding achieves strong secrecy, low distortion, and high perceptual quality simultaneously.

Problem

• secure rate-distortion-perception
• information leakage
• perceptual quality
• broadcast channels
• common randomness

Innovation

• secure rate-distortion-perception
• randomized coding
• common randomness
• broadcast channels
• side information