Welcome to the resource topic for 2021/733
Title:
GenoPPML – a framework for genomic privacy-preserving machine learning
Authors: Sergiu Carpov, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev
Abstract:We present a framework GenoPPML for privacy-preserving machine learning in the context of sensitive genomic data processing. The technology combines secure multiparty computation techniques based on the recently proposed Manticore secure multiparty computation framework for model training and fully homomorphic encryption based on TFHE for model inference. The framework was successfully used to solve breast cancer prediction problems on gene expression datasets coming from distinct private sources while preserving their privacy - the solution winning 1st place for both Tracks I and III of the genomic privacy competition iDASH’2020. Extensive benchmarks and comparisons to existing works are performed. Our 2-party logistic regression computation is 11\times faster than the one in De Cock et al. on the same dataset and it uses only a single CPU core.
ePrint: https://eprint.iacr.org/2021/733
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .