Differentially Private Accelerated Optimization Algorithms

Understand

We present two classes of differentially private optimization algorithms derived from the well-known accelerated first-order methods.

• The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy.
• The second class of algorithms are based on Nesterov's accelerated gradient method and its recent multi-stage variant.
• We propose a noise dividing mechanism for the iterations of Nesterov's method in order to improve the error behavior of the algorithm.