Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS.

ABSTRACT: The introduction of trusted execution environments (TEEs), such as secure enclaves provided by the Intel SGX technology has enabled secure and privacy-preserving computation on the cloud. The stringent resource limitations, such as memory constraints, required by some TEEs necessitates the development of computational approaches with reduced memory usage, such as sketching. One example is the SkSES method for GWAS on a cohort of case and control samples from multiple institutions, which identifies the most significant SNPs in a privacy-preserving manner without disclosing sensitive genotype information to other institutions or the cloud service provider. Here we show how to improve the performance of SkSES on large datasets by augmenting it with a learning-augmented approach. Specifically,

SUBMITTER: Xu J 

PROVIDER: S-EPMC11451619 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature