We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general applicability to a wide range of probabilistic modelling problems by implementing the differentially private variational inference algorithm, allowing users to fit any parametric probabilistic model with a differentiable density function. d3p adopts the probabilistic programming paradigm as a powerful way for the user to flexibly define such models. We demonstrate the use of our software on a hierarchical logistic regression example, showing the expressiveness of the modelling approach as well as the ease of running the parameter inference. We also perform an empirical evaluation of the runtime of the private inference on a complex model and find an $\sim$10 fold speed-up compared to an implementation using TensorFlow Privacy.

翻译：我们提出d3p,这是一个软件包,旨在帮助在不同的隐私保障下对运行时有效、广泛适用的贝耶斯人推断。 d3p通过实施差别化的私人变式推断算法,使用户能够将任何参数性概率模型与不同的密度功能相匹配,从而对范围广泛的概率建模问题普遍适用。 d3p采用概率制程范式,作为用户灵活界定此类模型的有力方法。我们用等级级物流回归示例展示了我们的软件,显示了建模方法的表达性以及运行参数推断的易易性。我们还对复杂模型的私人推断运行时间进行了经验评估,并找到了与使用TensorFlow Pericy的操作相比的10美元折叠速度。