Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes

2026-07-03

AI Summary

This work addresses the lack of efficient compression mechanisms for high-dimensional data, such as images, under differential privacy, which leads to substantial storage overhead and limited practicality. The authors propose DP-DiPP, a novel framework that uniquely integrates Poisson Private Representations (PPR) with the diffusion-based compression method DiffC, leveraging stochastic encoding and diffusion models to achieve a flexible trade-off among privacy, compression ratio, and utility. Evaluated on private image classification using CIFAR-10, DP-DiPP achieves 10–30 times higher compression ratios compared to existing baselines while maintaining comparable privacy guarantees and model utility.

Abstract

The ever-increasing collection of personal data has created mounting pressure to develop technologies that protect sensitive aspects of individual identity. Differential privacy (DP) provides a principled framework with strong formal guarantees and has already achieved practical success. However, releasing high-dimensional data, such as images, has remained elusive: releasing uncompressed privatized data requires significant storage. At the same time, no effective data compression scheme exists that can compress high-resolution data with privacy guarantees. We address this challenge with DP-DiPP, a compression pipeline that combines stochastic codes with diffusion models. DP-DiPP is highly flexible: the practitioner has direct control over the compression rate-privacy-utility tradeoff. As the theoretical backbone, we extend the Poisson private representation (PPR) to encode the outputs of privacy mechanisms. We then combine it with DiffC, a diffusion-based lossy data compression method, to obtain a differentially private image compressor. Our experiments on privatized image classification on CIFAR-10 demonstrate that DP-DiPP significantly outperforms the baseline, achieving a 10-30 times better compression while retaining comparable privacy guarantees and utility.

Problem

• differential privacy
• data compression
• high-dimensional data
• image compression
• privacy-preserving

Innovation

• differential privacy
• diffusion models
• stochastic codes
• data compression
• privacy-preserving machine learning