Shuffling the deck for privacy
By integrating an ensemble of privacy-preserving algorithms, a KAUST research team has developed a machine-learning approach that addresses a significant challenge in medical research: How to use the power of artificial intelligence (AI) to accelerate discovery from genomic data while protecting the privacy of individuals.[1]
“Omics data usually contains a lot of private information, such as gene expression and cell composition, which could often be related to a person’s disease or health status,” says KAUST’s Xin Gao. “AI models trained on this data – particularly deep learning models – have the potential to retain private ...











