论文标题
DP-SGD的私人广告建模
Private Ad Modeling with DP-SGD
论文作者
论文摘要
隐私保护ML的著名算法是私人随机梯度下降（DP-SGD）。尽管该算法已在文本和图像数据上进行了评估，但以前尚未将其应用于ADS数据，这些数据以其高级失衡和稀疏梯度更新而臭名昭著。在这项工作中，我们将DP-SGD应用于几个广告建模任务，包括预测点击率，转换率和转换事件的数量，并评估其在现实世界中数据集中的隐私性权衡权衡。我们的工作是第一个从经验上证明DP-SGD可以为广告建模任务提供隐私和实用性的工作。
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.
