DPGOMI: 基于高斯优化模型反演的差分隐私数据发布
DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion
AI总结：
针对高维敏感数据发布的隐私保护需求，提出DPGOMI方法，结合公开生成器映射与低维DP-GAN，在同等隐私水平下提升了数据生成质量与分类性能。
摘要：
高维数据在深度学习时代应用广泛，但部分含敏感信息的数据在无隐私保护的情况下不允许被共享。本文提出一种名为DPGOMI（基于高斯优化模型反演的差分隐私数据发布）的新型差分隐私数据发布方法以解决该问题。该方法先利用公开生成器将隐私数据映射到隐空间，再采用收敛性更优的低维DP-GAN（差分隐私生成对抗网络）处理。我们在标准数据集CIFAR10和SVHN上评估了DPGOMI的性能。结果表明，在提供同等隐私水平的前提下，DPGOMI在Inception Score（ inception得分）、Fréchet Inception Distance（弗雷歇 inception距离）以及分类性能上均优于标准DP-GAN方法，为GAN训练中保护敏感数据同时维持高质量输出提供了可行方案。
英文摘要：
High-dimensional data are widely used in the era of deep learning with numerous applications. However, certain data which has sensitive information are not allowed to be shared without privacy protection. In this paper, we propose a novel differentially private data releasing method called Differentially Private Data Publishing with Gaussian Optimized Model Inversion (DPGOMI) to address this issue. Our approach involves mapping private data to the latent space using a public generator, followed by a lower-dimensional DP-GAN with better convergence properties. We evaluate the performance of DPGOMI on standard datasets CIFAR10 and SVHN. Our results show that DPGOMI outperforms the standard DP-GAN method in terms of Inception Score, Fréchet Inception Distance, and classification performance, while providing the same level of privacy. Our proposed approach offers a promising solution for protecting sensitive data in GAN training while maintaining high-quality results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
