Salīdzināt metodes

	Diferenciālā privātums	Stohastiskā gradienta metode (SGD)
Nozare	Privātums	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2006	1951
Autors	Cynthia Dwork	Robbins, H. & Monro, S.
Tips	Privacy-preserving randomized mechanism	First-order iterative optimization algorithm
Pirmavots	Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.
Citi nosaukumi	DP, epsilon-differential privacy, randomized privacy, Diferansiyel Gizlilik	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent
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.	Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.