Cryptology ePrint Archive: Report 2021/733

GenoPPML – a framework for genomic privacy-preserving machine learning

Sergiu Carpov and Nicolas Gama and Mariya Georgieva and 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.

Category / Keywords: applications / privacy-preserving machine learning, multi-party computation, homomorphic encryption, genomic privacy, differential privacy

Date: received 1 Jun 2021

Contact author: sergiu at inpher io, nicolas at inpher io, mariya at inpher io, dimitar at inpher io

Available format(s): PDF | BibTeX Citation

Version: 20210603:140028 (All versions of this report)

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