Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

With the rapid evolution of the Internet, the application of artificial intelligence fields is more
and more extensive, and the era of AI has come. At the same time, adversarial attacks in the …

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

Federated Learning is the current state-of-the-art in supporting secure multi-party machine
learning (ML): data is maintained on the owner's device and the updates to the model are …

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data
collaboration without revealing their private data to each other. Recently, vertical FL, where …

Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly
evaluate a function on their private inputs while maintaining input privacy. In this work, we …

Due to the strong analytical ability of big data, deep learning has been widely applied to
model on the collected data in industrial Internet of Things (IoT). However, for privacy issues …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep
Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both …