Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

We consider training models on private data that are distributed across user devices. To
ensure privacy, we add on-device noise and use secure aggregation so that only the noisy …

This paper presents LDP-Fed, a novel federated learning system with a formal privacy
guarantee using local differential privacy (LDP). Existing LDP protocols are developed …

We consider a distributed empirical risk minimization (ERM) optimization problem with
communication efficiency and privacy requirements, motivated by the federated learning …

We consider training models with differential privacy (DP) using mini-batch gradients. The
existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD) …

Data privacy arises as one of the most important concerns, facing the pervasive
commoditization of big data statistic analysis in Internet of Things (IoT). Current solutions are …