基于联合学习和同态加密的云平台的隐私保护优化方法
1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了异质联合同型加密云 (HFHE-Cloud) 模型,增强分布式培训的安全性和效率. 在多租户云环境中,HFHE-Cloud实现了高隐私保护和计算性能.
科学领域:
- 云计算 云计算 云计算
- 分布式系统 分布式系统
- 密码学 密码学 密码学 密码学
- 机器学习 机器学习
背景情况:
- 云环境中的分布式培训面临着隐私泄露,通信冗余和由于多租户和高并发性而降低聚合效率等挑战.
- 现有的联合学习模型在复杂的云基础设施中努力平衡隐私保护和计算性能.
研究的目的:
- 为云平台开发安全高效的协作学习框架,以优化隐私保护和计算性能.
- 解决与加密计算和分布式培训中的通信延迟相关的性能瓶.
主要方法:
- 提出了异质联合同型加密云 (HFHE-Cloud) 模型,将联合学习 (FL) 与同型加密集成.
- 实施了对异质节点的层次关键映射和动态调度机制,以减少性能瓶.
- 通过在协作培训过程中不暴露原始数据来确保数据隐私.
主要成果:
- 在综合性能方面,HFHE-Cloud显著超过了基线模型 (FedAvg,FedProx,FedPer,FedNova,HE-FedAvg) 的表现.
- 实现了高度的隐私保护,全球准确度高达94.25%,稳定的损失在0.09.
- 通过将加密/解密时间缩短约三分之一,提高计算性能,加密开销13%.
- 通过将通信轮减少约五分之一并保持90%以上的节点参与,提高了分布式培训效率.
结论:
- 该HFHE-云模型展示了高安全性和可扩展性,适合多租户云环境.
- 为云服务提供商和寻求强大隐私保护和高效协作培训的企业提供可行的技术解决方案.
- 在云平台上成功平衡了密集的隐私要求与高效的分布式学习性能.
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