Neural networks (NNs) have become one of the most important tools for artificial
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …

Data confidentiality is a central concern in modern computer systems and services, as
sensitive data from users and companies are being increasingly delegated to such systems …

We implement training of neural networks in secure multi-party computation (MPC) using
quantization commonly used in said setting. We are the first to present an MNIST classifier …

Federated Learning (FL) enhances decentralized machine learning by safeguarding data
privacy, reducing communication costs, and improving model performance with diverse data …

We introduce Avocado, a secure in-memory distributed storage system that provides strong
security, fault-tolerance, consistency (linearizability) and performance for untrusted cloud …

Data privacy is of great concern in cloud machine-learning service platforms when sensitive
data are exposed to service providers. While private computing environments (eg secure …

Algorithmic discrimination is one of the significant concerns in applying machine learning
models to a real-world system. Many researchers have focused on developing fair machine …

Data, when coupled with state-of-the-art machine learning models, can enable remarkable
applications. But, there exists an underlying tension: users wish to keep their data private …

Online learning is widely used in production to refine model parameters after initial
deployment. This opens several vectors for covertly launching attacks against deployed …

Many organizations own data but have limited machine learning expertise (data owners). On
the other hand, organizations that have expertise need data from diverse sources to train …