An integrated shuffler optimizes the privacy of personal genomic data used for machine learning

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.

algorithms artificial artificial intelligence challenge data discovery ensemble genomic genomic data intelligence machine machine learning medical medical research power privacy research research team security team