In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity and interest. Though unrivaled in versatility, DP-SGD requires a non-trivial privacy overhead (for privately tuning the model's hyperparameters) and a computational complexity which might be extravagant for simple models such as linear and logistic regression. This paper revamps the objective perturbation mechanism with tighter privacy analyses and new computational tools that boost it to perform competitively with DP-SGD on unconstrained convex generalized linear problems.

翻译：在隐私保护机器学习领域，差分隐私随机梯度下降法（DP-SGD）在流行度和关注度上已超越目标扰动机制。尽管DP-SGD在通用性上无可匹敌，但其需要非平凡的隐私开销（用于私下调整模型超参数），且对于线性回归和逻辑回归等简单模型而言，计算复杂度可能过高。本文通过更严格的隐私分析与新型计算工具对目标扰动机制进行全面改进，使其在无约束凸广义线性问题上能与DP-SGD竞争。