Recent developments in a variety of sectors, including health care, research and the direct-
to-consumer industry, have led to a dramatic increase in the amount of genomic data that …

The sharing of genomic data holds great promise in advancing precision medicine and
providing personalized treatments and other types of interventions. However, these …

While rich medical, behavioral, and socio-demographic data are key to modern data-driven
research, their collection and use raise legitimate privacy concerns. Anonymizing datasets …

We propose Falcon, an end-to-end 3-party protocol for efficient private training and
inference of large machine learning models. Falcon presents four main advantages-(i) It is …

In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …

Secure multi-party computation (MPC) allows a group of mutually distrustful parties to
compute a joint function on their inputs without revealing any information beyond the result …

Genome-wide association studies (GWASs) seek to identify genetic variants associated with
a trait, and have been a powerful approach for understanding complex diseases. A critical …

Using real-world evidence in biomedical research, an indispensable complement to clinical
trials, requires access to large quantities of patient data that are typically held separately by …

Real-world healthcare data sharing is instrumental in constructing broader-based and larger
clinical datasets that may improve clinical decision-making research and outcomes …

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 …