Many organizations wish to collaboratively train machine learning models on their combined
datasets for a common benefit (eg, better medical research, or fraud detection). However …

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 …

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 …

Secure multi-party computation (MPC) allows parties to perform computations on data while
keeping that data private. This capability has great potential for machine-learning …

Background In biomedical applications, valuable data is often split between owners who
cannot openly share the data because of privacy regulations and concerns. Training …

Collaborative and federated learning has become an emerging solution to many industrial
applications where data values from different sites are exploit jointly with privacy protection …

Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine
learning (ML). However, secure training of large-scale ML models currently requires a …

Machine learning benefits from large training datasets, which may not always be possible to
collect by any single entity, especially when using privacy-sensitive data. In many contexts …

Multi-party computation (MPC) allows distributed machine learning to be performed in a
privacy-preserving manner so that end-hosts are unaware of the true models on the clients …

We introduce CryptGPU, a system for privacy-preserving machine learning that implements
all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in …