Porovnať metódy

	Homomorfné šifrovanie	Diferenciálna privatnosť
Odbor	Súkromie	Súkromie
Rodina	Machine learning	Machine learning
Rok vzniku	2009	2006
Tvorca	Craig Gentry	Cynthia Dwork
Typ	Lattice-based cryptographic scheme	Privacy-preserving randomized mechanism
Pôvodný zdroj	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.
Ďalšie názvy	FHE, Fully Homomorphic Encryption, Leveled Homomorphic Encryption, Homomorfik Şifreleme	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik
Príbuzné	3	3
Zhrnutie	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.