Abstract:Differential private optimization for nonconvex smooth objective is considered. In the previous work, the best known utility bound is O( √ d/(nε DP )) in terms of the squared full gradient norm, which is achieved by Differential Private Gradient Descent (DP-GD) as an instance, where n is the sample size, d is the problem dimensionality and ε DP is the differential privacy parameter. To improve the best known utility bound, we propose a new differential private optimization framework called DIFF2 (DIFFerential … Show more
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