June 21, 2021

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GenoPPML – a framework for genomic privacy-preserving machine learning, by Sergiu Carpov and Nicolas Gama and Mariya Georgieva and Dimitar Jetchev

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.