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arXiv · 2607.03392

Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes

Gergely Flamich·Oykü Sıla Güner·Yanxiao Liu·Deniz Gündüz
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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.

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Gergely Flamich, Oykü Sıla Güner, Yanxiao Liu, Deniz Gündüz. 2026-07-03. Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes. https://arxiv.org/abs/2607.03392

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