Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams

TL;DR

This paper develops an optimal matrix mechanism for differential privacy over adaptive streams, enabling more efficient private machine learning models, especially in federated learning scenarios.

Contribution

It introduces a parameter-free fixed-point algorithm for optimal matrix factorizations tailored to adaptive streams, advancing differential privacy techniques in machine learning.

Findings

Significant improvements in federated learning with user-level differential privacy.

Theoretical results on matrix factorizations for adaptive streams.

Effective instantiation of the framework for practical matrices.

Abstract

Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting. We prove fundamental theoretical results on the applicability of matrix factorizations to adaptive streams, and provide a parameter-free fixed-point algorithm for computing optimal factorizations. We instantiate this framework with respect to concrete matrices which arise naturally in machine learning, and train user-level differentially private models with the resulting optimal mechanisms, yielding significant improvements in a notable problem in federated learning with user-level differential privacy.