Salīdzināt metodes

	Diferenciālā privātums	Drošas daudzpusējās skaitļošanas (Secure Multi-Party Computation - SMPC) metodes
Nozare	Privātums	Privātums
Saime	Machine learning	Machine learning
Izcelsmes gads	2006	1982
Autors	Cynthia Dwork	Andrew Yao
Tips	Privacy-preserving randomized mechanism	Cryptographic protocol family
Pirmavots	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.	Yao, A. C. (1982). Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science, 160–164.
Citi nosaukumi	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik	MPC, Multi-Party Computation, Privacy-Preserving Computation, Güvenli Çok Taraflı Hesaplama
Saistītās	3	3
Kopsavilkums	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.	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.