English(EN) Differentially Private Paired Table-Image Multimodal Synthesis

新框架支持配对表-图像数据的差分隐私合成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了DP-TabImage，一个用于合成配对的表格和图像数据并保持差分隐私的新框架。该方法解决了在应用隐私技术时保持模态依赖性的挑战，因为这些技术通常为每种数据类型采用不同的机制。DP-TabImage利用私有的概率图模型处理表格数据，并使用DP-SGD训练的表条件扩散模型生成图像，从而在表格保真度、图像保真度和跨模态对齐之间取得了平衡。 AI

影响能够更安全、更私密地生成用于AI训练的复杂多模态数据集。

排序理由该集群包含一篇详细介绍数据合成新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Kai Chen, Josephine Lamp, Somesh Jha, Tianhao Wang · 2026-09-02 04:00

差分隐私配对表-图像多模态合成

arXiv:2609.00708v1 Announce Type: cross Abstract: Differentially private (DP) synthesis has been extensively studied for tabular and image data separately, yet many real-world datasets contain images paired with multivariate tabular records. Synthesizing such data is particularly…

本文如何被排名

报道来源 [1]

差分隐私配对表-图像多模态合成

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