English(EN) Exploring the Privacy--Size Tradeoff for Differentially Private Self-Attentive Variational Autoencoders

新型SAVANT模型在隐私和效用之间取得平衡，适用于表格数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了SAVANT，这是一种新颖的、用于表格数据的自注意力变分自编码器，集成了差分隐私（DP-SGD）。该模型是首个将注意力机制、VAE和差分隐私结合用于表格数据的模型。在公共数据集上的评估表明，在相同的隐私保证下，增加数据量通常会提高下游分类器的效用，并且SAVANT的表现优于PrivBayes和MST等其他边际机制。成员推理攻击未发现聚合成员信号，表明隐私得到有效保护。 AI

影响引入了一种用差分隐私生成合成表格数据的新方法，有可能在保护隐私的同时改善数据共享。

排序理由学术论文，详细介绍了一种新模型及其评估。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Rohit Sadavarte, Dhruvi Kothari, Sparsh Gupta, Eric Van Oeveren · 2026-11-05 05:00

探索差分隐私自注意力变分自编码器的隐私-规模权衡

arXiv:2611.05582v1 Announce Type: new Abstract: Data tends to be most useful when it is shared, and the data most worth sharing is often the data that privacy obligations restrict. Synthetic data from a generator trained under differential privacy (DP) is one way to reconcile the…

本文如何被排名

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探索差分隐私自注意力变分自编码器的隐私-规模权衡

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