DP-DiPP: Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes
Score
79提出全新结合扩散模型与随机码的差分隐私压缩框架，解决现有方法无法同时实现高效压缩与隐私保证的痛点。
理论推导与实验验证完整，方法描述清晰，但缺少更大规模数据集（如ImageNet）的测试，影响说服力。
在图像存储、医疗数据发布等场景有直接应用潜力，且用户可调节权衡，实用性强。
论文发表于arXiv当天，内容紧跟隐私保护与扩散模型前沿，时效性极强。
来源为arXiv预印本但属于计算机科学顶级子库AI/CL/LG，且方法原创性强，信源可信。
Base 87.5 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
High methodological novelty with significant experimental results, though generalization to larger datasets needs further validation.
AI commentary
「What makes this paper stand out is its clever fusion of diffusion models' generative power with stochastic codes' privacy properties, achieving over 10x compression improvement. For AI researchers and engineers working on privacy-preserving computing, data compression, and image processing, DP-DiPP offers a directly applicable framework that could address real-world bottlenecks in storing and transmitting high-dimensional private data.」
Quick overview
This paper proposes DP-DiPP, a compression pipeline combining diffusion models with stochastic codes to address the challenge of compressing high-dimensional image data under differential privacy. It provides direct control over the compression rate-privacy-utility tradeoff, extends the Poisson Private Representation (PPR) to encode privacy mechanism outputs, and integrates with DiffC diffusion-based lossy compression. Experiments on CIFAR-10 demonstrate a 10-30x improvement in compression ratio over the baseline while maintaining comparable privacy guarantees and utility.
- Proposes DP-DiPP, the first compression pipeline combining diffusion models with stochastic codes for differentially private image compression.
- Extends Poisson Private Representation (PPR) to encode privacy mechanism outputs with theoretical guarantees.
- Achieves 10-30x better compression ratio on CIFAR-10 image classification compared to baseline.
- Allows flexible trade-off control among compression rate, privacy, and utility.
Full text
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