为神经网络使用同型加密的高效密钥设计
Youyeon Joo1, Seungjin Ha1, Hyunyoung Oh2
1Department of Electrical and Computer Engineering & ISRC, Seoul National University, Seoul 08826, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究通过重新设计旋转键盘来优化完全同型加密 (FHE) 以保护隐私的机器学习 (PPML). 新设计显著降低了内存使用量,并加快了对加密数据的安全机器学习的计算速度.
科学领域:
- 密码学 密码学 密码学 密码学
- 机器学习 机器学习
- 数据安全 数据安全
背景情况:
- 物联网 (IoT) 生成敏感数据,增加对机器学习作为服务 (MLaaS) 的依赖.
- 越来越多的隐私问题需要隐私保护机器学习 (PPML).
- 完全同型加密 (FHE) 可以对加密数据进行计算,但面临效率挑战.
研究的目的:
- 为解决基于FHE的神经网络推断中的计算开销.
- 为了优化旋转键盘设计,提高FHE效率.
- 为了减少PPML中的内存消耗和计算成本.
主要方法:
- 系统地探索三个关键设计空间 (KDS) 进行旋转键盘优化.
- 开发一个优化的旋转键盘组.
- 通过两个案例研究进行评估,证明了记忆和速度的改善.
主要成果:
- 实现了高达11.29倍的内存缩小.
- 在基于FHE的神经网络推断中,已经证明了1.67x到2.55x的加快速度.
- 验证了拟议的KDS设计的有效性.
结论:
- 优化旋转键盘设计是一种可行的策略,可以提高PPML的FHE效率.
- 拟议的KDS方法提供了显著的内存和计算优势.
- 这项工作有助于更实用,更有效的保护隐私的机器学习解决方案.
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