Using large, multi-national datasets for high-performance medical imaging AI systems
requires innovation in privacy-preserving machine learning so models can train on sensitive …

Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine
learning (PPML). Several PPML models have been proposed based on various FHE …

Background Data science offers an unparalleled opportunity to identify new insights into
many aspects of human life with recent advances in health care. Using data science in …

Data privacy concerns are increasing significantly in the context of the Internet of Things,
cloud services, edge computing, artificial intelligence applications, and other applications …

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 …

When applying machine learning to sensitive data, one has to find a balance between
accuracy, information security, and computational-complexity. Recent studies combined …

Machine learning algorithms have achieved remarkable results and are widely applied in a
variety of domains. These algorithms often rely on sensitive and private data such as …

Increasingly, modern Artificial Intelligence (AI) research has become more computationally
intensive. However, a growing concern is that due to unequal access to computing power …

Current sound-based practices and systems developed in both academia and industry point
to convergent research trends that bring together the field of sound and music Computing …

We studied 53 privacy-preserving neural-network papers in 2016-2022 based on
cryptography (without trusted processors or differential privacy), 16 of which only use …