方法对比
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| 同态加密× | 安全多方计算× | |
|---|---|---|
| 领域 | 隐私 | 隐私 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2009 | 1982 |
| 提出者≠ | Craig Gentry | Andrew Yao |
| 类型≠ | Lattice-based cryptographic scheme | Cryptographic protocol family |
| 开创性文献≠ | Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. ACM Symposium on Theory of Computing (STOC), 169–178. DOI ↗ | Yao, A. C. (1982). Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science, 160–164. DOI ↗ |
| 别名 | FHE, Fully Homomorphic Encryption, Leveled Homomorphic Encryption, Homomorfik Şifreleme | MPC, Multi-Party Computation, Privacy-Preserving Computation, Güvenli Çok Taraflı Hesaplama |
| 相关 | 3 | 3 |
| 摘要≠ | Homomorphic Encryption (HE) is a cryptographic framework that allows arbitrary computations to be performed directly on encrypted data without requiring decryption. First realized as a fully general construction by Craig Gentry in 2009 using ideal lattices, it enables a server to process sensitive data and return an encrypted result that, when decrypted by the data owner, equals the result of performing the same computation on the plaintext. It is foundational to privacy-preserving machine learning, secure cloud computing, and confidential analytics. | Secure Multi-Party Computation (SMPC) is a cryptographic paradigm that enables two or more parties to jointly compute a function over their private inputs without revealing those inputs to one another. Introduced by Andrew Yao in 1982 through his seminal garbled-circuit construction, SMPC provides provable privacy guarantees grounded in computational hardness assumptions. It underpins modern privacy-preserving data analysis, enabling collaborative computation on sensitive datasets in finance, healthcare, and machine learning. |
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