"Shuffling the Deck for Privacy"
"Shuffling the Deck for Privacy"
A KAUST research team has developed a Machine Learning (ML) approach that addresses a major medical research challenge by integrating an ensemble of privacy-preserving algorithms. The challenge is using the power of Artificial Intelligence (AI) to accelerate genomic data discovery while protecting individuals' privacy. According to KAUST's Xin Gao, omics data typically contains a large amount of private information, such as gene expression and cell composition. This information can often be linked to a person's disease or health status.