So sánh phương pháp

	Mã hóa đồng cấu	Quyền riêng tư vi phân
Lĩnh vực	Quyền riêng tư	Quyền riêng tư
Họ	Machine learning	Machine learning
Năm ra đời	2009	2006
Người khởi xướng	Craig Gentry	Cynthia Dwork
Loại	Lattice-based cryptographic scheme	Privacy-preserving randomized mechanism
Công trình gốc	Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. ACM Symposium on Theory of Computing (STOC), 169–178.	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.
Tên gọi khác	FHE, Fully Homomorphic Encryption, Leveled Homomorphic Encryption, Homomorfik Şifreleme	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Liên quan	3	3
Tóm tắt	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.	Differential privacy is a mathematical framework for releasing statistical information about a dataset while providing rigorous guarantees that individual records cannot be identified or inferred. Introduced by Cynthia Dwork in 2006, it formalizes privacy as a probabilistic bound: any single individual's presence or absence in the dataset changes the output distribution by at most a multiplicative factor of e^ε, where ε is the privacy budget controlling the privacy–utility tradeoff.