PQSF:后量子安全隐私保护联合学习.
Xia Zhang1, Haitao Deng2, Rui Wu2
1Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
Scientific reports
|October 9, 2024
概括
本研究介绍了Improved-Pilaram,这是一个基于格子的新型秘密共享计划,增强了联合学习 (FL) 中的隐私. 新的后量子安全FL方案 (PQSF) 减少了20%的通信和计算开销.
科学领域:
- 密码学 密码学 密码学 密码学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 秘密共享对于联邦学习 (FL) 中的隐私至关重要.
- 现有的FL隐私计划面临量子计算带来的风险,并且具有很高的开销.
- 皮拉拉姆和其他人. 该公司的多阶段秘密共享计划已知隐私泄露漏洞.
研究的目的:
- 设计一个安全高效的后量子联合学习方案.
- 为了解决现有的多阶段秘密共享方法中的隐私泄露风险.
- 在联合学习中降低通信和计算成本.
主要方法:
- 开发了一个基于格子的多阶段秘密共享计划 (改进的Pilaram).
- 提出了一个使用Improved-Pilaram的后量子安全联合学习方案 (PQSF).
- 实现了通过秘密共享实现模型参数加密和掩码重建的双重掩码.
主要成果:
- 改进的Pilaram允许基于公共载体的重建秘密价值,而不会改变秘密共享.
- PQSF有效地加密模型参数和重建面具.
- 改进的Pilaram的多阶段性质减少了频繁更新本地秘密共享的需要.
结论:
- 拟议的PQSF方案在联合学习中提供了针对量子威胁的增强安全性.
- 与现有解决方案相比,PQSF显著降低了约20%的通信复杂性和计算开销.
- 基于格子的方法为未来保护隐私的联合学习系统提供了坚实的基础.
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