Locally Optimal Private Sampling: Beyond the Global Minimax

Neural Information Processing Systems 

We study the problem of sampling from a distribution under local differential privacy (LDP). Given a private distribution P P, the goal is to generate a single sample from a distribution that remains close to P in f-divergence while satisfying the constraints of LDP.