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 …

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 …

Secure Multi-Party Computation (MPC) is an area of cryptography that enables computation
on sensitive data from multiple sources while maintaining privacy guarantees. However …

With the increasing public attention to data security and privacy protection, privacy-
preserving machine learning (PPML) has become a research hotspot in recent years …

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 …

Privacy issue is a main concern restricting data sharing and cross-organization
collaborations. While Privacy-Preserving Machine Learning techniques such as Multi-Party …

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 …

Secure Two-party Computation (2PC) allows two parties to compute any function on their
private inputs without revealing their inputs to each other. In the offline/on-line model for …

Privacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has
gained momentum in the recent past. Assuming a minimal network of pair-wise private …

Secure machine learning (ML) inference can provide meaningful privacy guarantees to both
the client (holding sensitive input) and the server (holding sensitive weights of the ML …