Deep learning recommendation models (DLRM) are extensively adopted to support many
online services. Typical DLRM training frameworks adopt the parameter server (PS) in CPU …

We propose a new family of label randomizers for training regression models under the
constraint of label differential privacy (DP). In particular, we leverage the trade-offs between …

In display advertising, predicting the conversion rate, that is, the probability that a user takes
a predefined action on an advertiser's website, such as purchasing goods is fundamental in …

With the impending removal of third-party cookies from major browsers and the introduction
of new privacy-preserving advertising APIs, the research community has a timely opportunity …

We propose a new family of label randomizers for training regression models under the
constraint of label differential privacy (DP). In particular, we leverage the trade-offs between …

Split learning is a popular technique used to perform vertical federated learning, where the
goal is to jointly train a model on the private input and label data held by two parties. To …

Motivated by problems arising in digital advertising, we introduce the task of training
differentially private (DP) machine learning models with semi-sensitive features. In this …

The Privacy Sandbox Attribution Reporting API has been recently deployed by Google
Chrome to support the basic advertising functionality of attribution reporting (aka conversion …