论文标题
通过自适应流的最佳私人线性操作员改善了SGD的差异隐私
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
论文作者
论文摘要
在最新的应用中，我们需要在自适应流上进行差异隐私，我们研究了在这种情况下矩阵机制的最佳实例化问题。我们证明了基本理论结果，这些结果是矩阵因素化对自适应流的适用性，并为计算最佳因素化提供了无参数的固定点算法。我们就机器学习中自然出现的混凝土矩阵实例化了该框架，并通过培训用户级别的私人模型，并具有最佳的最佳机制，从而在使用用户级别的差异隐私的联合学习中实现了显着的问题。
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.
