Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions. (arXiv:2312.08685v1 [cs.LG])

We examine a private ADMM variant for (strongly) convex objectives which is a
primal-dual iterative method. Each iteration has a user with a private function
used to update the primal variable, masked by Gaussian noise for local privacy,
without directly adding noise to the dual variable. Privacy amplification by
iteration explores if noises from later iterations can enhance the privacy
guarantee when releasing final variables after the last iteration. Cyffers et
al. [ICML 2023] explored privacy amplification by iteration for …

amplification function functions local noise objectives privacy private update variable