Machine learningPrivacy-preserving computation
同态加密
同态加密(Homomorphic Encryption, HE)是一种密码学框架,它允许直接在加密数据上执行任意计算,而无需解密。它于2009年由Craig Gentry首次通过理想格(ideal lattices)实现为一种完全通用的构造,使得服务器能够处理敏感数据并返回加密结果,数据所有者解密该结果后,即可得到与在明文上执行相同计算相同的结果。它是保护隐私的机器学习、安全云计算和机密分析的基础。
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来源
- Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. ACM Symposium on Theory of Computing (STOC), 169–178. DOI: 10.1145/1536414.1536440 ↗
如何引用本页
ScholarGate. (2026, June 2). Fully Homomorphic Encryption. ScholarGate. https://scholargate.app/zh/privacy/homomorphic-encryption
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