A Parallel Region-Adaptive Differential Privacy Framework for Image Pixelization

TL;DR

This paper introduces a parallel, region-adaptive differential privacy framework for image pixelization that improves efficiency and utility while maintaining strong privacy guarantees, suitable for real-time video applications.

Contribution

It presents a novel adaptive pixelization method combining differential privacy with GPU parallelism and lightweight storage, enhancing scalability and utility in privacy-preserving visual data processing.

Findings

Achieves significant runtime acceleration over classical methods

Reduces storage overhead with a lightweight scheme

Effectively prevents face re-identification attacks

Abstract

The widespread deployment of high-resolution visual sensing systems, coupled with the rise of foundation models, has amplified privacy risks in video-based applications. Differentially private pixelization offers mathematically guaranteed protection for visual data through grid-based noise addition, but challenges remain in preserving task-relevant fidelity, achieving scalability, and enabling efficient real-time deployment. To address this, we propose a novel parallel, region-adaptive pixelization framework that combines the theoretical rigor of differential privacy with practical efficiency. Our method adaptively adjusts grid sizes and noise scales based on regional complexity, leveraging GPU parallelism to achieve significant runtime acceleration compared to the classical baseline. A lightweight storage scheme is introduced by retaining only essential noisy statistics, significantly reducing space overhead. Formal privacy analysis is provided under the Laplace mechanism and parallel composition theorem. Extensive experiments on the PETS, Venice-2, and PPM-100 datasets demonstrate favorable privacy-utility trade-offs and significant runtime/storage reductions. A face re-identification attack experiment on CelebA further confirms the method's effectiveness in preventing identity inference. This validates its suitability for real-time privacy-critical applications such as elderly care, smart home monitoring, driver behavior analysis, and crowd behavior monitoring.