Concentrated Differentially Private Gradient Descent With Adaptive Per-Iteration Privacy Budget

Authors:
Jaewoo Lee University of Georgia
Daniel Kifer The Pennsylvania State University

Introduction:

Iterative algorithms' conversion to differentially private algorithms is often naive.

Abstract:

Iterative algorithms, like gradient descent, are common tools for solving a variety of problems, such as model fitting. For this reason, there is interest in creating differentially private versions of them. However, their conversion to differentially private algorithms is often naive. For instance, a fixed number of iterations are chosen, the privacy budget is split evenly among them, and at each iteration, parameters are updated with a noisy gradient.

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