3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses
Shahab Asoodeh, Mario Diaz
The Noisy-SGD algorithm is widely used for privately training machine learning models. Traditional privacy analyses of this algorithm assume that the internal state is publicly rev…
cs.CR2022★ 3 cited
The Saddle-Point Accountant for Differential Privacy
Wael Alghamdi, Shahab Asoodeh, Flavio P. Calmon +4
We introduce a new differential privacy (DP) accountant called the saddle-point accountant (SPA). SPA approximates privacy guarantees for the composition of DP mechanisms in an acc…