A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD). While this algorithm has been evaluated on text and image data, it has not been previously applied to ads data, which are notorious for their high class imbalance and sparse gradient updates. In this work we apply DP-SGD to several ad modeling tasks including predicting click-through rates, conversion rates, and number of conversion events, and evaluate their privacy-utility trade-off on real-world datasets. Our work is the first to empirically demonstrate that DP-SGD can provide both privacy and utility for ad modeling tasks.

翻译：隐私保护机器学习中一个著名的算法是差分隐私随机梯度下降（DP-SGD）。尽管该算法已在文本和图像数据上得到评估，但此前尚未应用于广告数据——这类数据以高度类别不平衡和梯度更新的稀疏性著称。在本工作中，我们将DP-SGD应用于多个广告建模任务，包括预测点击率、转化率以及转化事件数量，并在真实数据集上评估其隐私-效用的权衡。我们的工作首次通过实证表明，DP-SGD能够在广告建模任务中同时提供隐私保护与模型效用。