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 …

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 …

With the ever-evolving concerns on privacy protection, vertical federated learning (FL),
where participants own non-overlapping features for the same set of instances, is becoming …

In privacy-preserving cross-device federated learning, users train a global model on their
local data and submit encrypted local models, while an untrusted central server aggregates …

Multisite medical data sharing is critical in modern clinical practice and medical research.
The challenge is to conduct data sharing that preserves individual privacy and data utility …

We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-
honest model with dishonest majority that is based on multiparty homomorphic encryption …

Many organizations need large amounts of high quality data for their applications, and one
way to acquire such data is to combine datasets from multiple parties. Since these …

In this paper, we present the first Federated Learning (FL) framework which is secure
against both confidentiality and integrity threats from the aggregation server, in the case …