Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry

Neural Information Processing Systems 

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of (ϵ, δ)- differential privacy (DP) in both Euclidean and non-Euclidean setups.

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