We present privacy-preserving solutions for Genome-Wide Association Studies (GWAS) based on Secure Multi-Party Computation (SMPC).
ABSTRACT. We present privacy-preserving solutions for Genome-Wide Asso- ciation Studies (GWAS) based on Secure Multi-Party Computation. (SMPC).
The goal of this thesis is to implement statistical computations for GWAS (e.g. χ˛-, p-, g-, and KS-tests) in ABY, a widely used state-of-the-art framework for ...
... Large-Scale Privacy-Preserving Statistical. Computations for Distributed Genome-Wide Association Studies in ABY” that will be jointly supervised by Christian.
We present privacy-preserving solutions for Genome-Wide Association Studies (GWAS) based on Secure Multi-Party Computation (SMPC).
Large-scale privacy-preserving statistical computations for distributed genome-wide association studies. Oleksandr Tkachenko, Thomas Schneider, Christian ...
W. Lu Y. Yamada and J. Sakuma . 2015. Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption. In BMC Medical ...
Large-Scale Privacy-Preserving Statistical Computations for Distributed Genome-Wide Association Studies ; 2018 · Conference Proceedings · 13. ACM Asia Conference ...
Oct 19, 2023 · This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the ...
Oct 3, 2023 · This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the ...