一个通信效率高的分布式矩阵乘法方案,具有隐私,安全和弹性
Tao Wang1, Zhiping Shi1, Juan Yang2,3
1National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Entropy (Basel, Switzerland)
|September 27, 2024
概括
我们介绍了使用痕迹多项式的新型安全分布式矩阵乘法 (SDMM) 方案. 这些计划提高了隐私,安全和弹性,同时减少了分布式学习的通信开销.
科学领域:
- 密码学和分布式系统
- 信息理论和编码.
背景情况:
- 安全分布式矩阵乘法 (SDMM) 对分布式学习算法至关重要.
- 现有的计划面临隐私,安全和滞后者弹性方面的挑战.
- 里德-所罗门 (RS) 代码为数据修复和秘密共享提供了洞察力.
研究的目的:
- 提出新的SDMM方案,利用微量多项式.
- 为了增强信息理论上的隐私,防止服务器勾结.
- 为了提供对拜占庭服务器的安全性和对滞后者的弹性.
主要方法:
- 在SDMM方案设计中使用痕迹多项式.
- 从Reed-Solomon (RS) 代码修复中调整原则.
- 制定方案来解决隐私,拜占庭和滞后者问题.
主要成果:
- 与现有方法相比,实现了较低的通信开销.
- 引入了提供信息理论隐私的计划.
- 提供了对拜占庭服务器的安全性和对滞后者的弹性.
- 证明了减少子包装和更低的服务器数量要求.
结论:
- 提出的基于痕迹多项式的SDMM方案为安全和高效的分布式学习提供了全面的解决方案.
- 这些方案显著降低了通信开销,计算复杂性和下载成本.
- 这项工作开创了基于微量多项式SDMM的安全性和弹性集成.
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