KEYWORDS : Genome-wide association study (GWAS), Confidential computing, Privacy-preserving, Intel SGX, Secure multi-party computation
DESCRIPTION : A privacy-preserving, population-scale genome-wide association study (GWAS) tool enabling collaborative analysis across multiple institutions using confidential computing. It employs optimizations like streaming, batching, and data parallelization on Intel SGX-based platforms to support linear and logistic regression efficiently while protecting against side-channel attacks.
ARROW_SUMMARY : Genomic data from multiple institutions → Confidential computing (Intel SGX) with optimized linear/logistic regression → Privacy-preserving GWAS results using streaming, batching, and parallelization