Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives

• URL: http://arxiv.org/abs/2002.09609v3
• Date: Tue, 17 Nov 2020 05:30:43 GMT
• Title: Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives
• Authors: Raman Arora, Teodor V. Marinov, Enayat Ullah
• Abstract summary: We propose an algorithm based on noisy mirror which achieves a first-order descent, inversely in the regime when the privacy parameter is proportional to the number of samples.
• Score: 28.99826590351627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we revisit the problem of private stochastic convex optimization. We propose an algorithm based on noisy mirror descent, which achieves optimal rates both in terms of statistical complexity and number of queries to a first-order stochastic oracle in the regime when the privacy parameter is inversely proportional to the number of samples.

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