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English(EN) Cocoon: A System Architecture for Differentially Private Training with Correlated Noises

新的Cocoon架构提高了差分隐私机器学习训练的效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Cocoon，一种旨在提高差分隐私机器学习训练效率的新系统架构。该方法通过利用相互抵消的相关噪声，解决了差分隐私通常伴随的准确性下降问题。Cocoon优化了跨CPU、GPU和专用近存储器处理（NMP）设备的大噪声历史记录处理，并包含针对稀疏嵌入表的特定优化。在基于FPGA的NMP原型上进行的测试表明，性能提升了1.23倍至10.82倍。 AI

影响 增强了隐私保护的机器学习训练，可能促使对敏感数据分析的更广泛应用。

排序理由 该集群描述了一篇详细介绍机器学习新系统架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的Cocoon架构提高了差分隐私机器学习训练的效率

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该集群描述了一篇详细介绍机器学习新系统架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Donghwan Kim, Xin Gu, Jinho Baek, Timothy Lo, Younghoon Min, Kwangsik Shin, Jongryool Kim, Jongse Park, Kiwan Maeng ·

    Cocoon：一种具有相关噪声的差分隐私训练的系统架构

    arXiv:2510.07304v2 Announce Type: replace-cross Abstract: Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differential privacy (DP) have been gaining attention as a solution. However, these algo…

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