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GenoPPML - a framework for genomic privacy-preserving machine learning [article]

Sergiu Carpov, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev
2021 IACR Cryptology ePrint Archive  
We present a framework GenoPPML for privacy-preserving machine learning in the context of sensitive genomic data processing.  ...  place for both Tracks I and III of the genomic privacy competition iDASH'2020.  ...  Conclusions and perspectives In this paper, we have introduced GenoPPML; an end-to-end framework for privacy-preserving machine learning applied to genomic data.  ... 
dblp:journals/iacr/CarpovGGJ21 fatcat:yiglzptvnnc35lweezhu2lp4qa

A Sequence Obfuscation Method for Protecting Personal Genomic Privacy

Shibiao Wan, Jieqiong Wang
2022
To tackle these problems, this paper proposes a sequence similarity-based obfuscation method, namely IterMegaBLAST, for fast and reliable protection of personal genomic privacy.  ...  Existing genomic privacy-protection methods are either time-consuming for encryption or with low accuracy of data recovery.  ...  Genoppmla Framework for Genomic Privacy-Preserving Machine Learning. Cryptology ePrint Archive . Chen, J., Wang, W. H., and Shi, X. (2020).  ... 
doi:10.3389/fgene.2022.876686 pmid:35495121 pmcid:PMC9043694 fatcat:a2i42bflzveynjcrlwf7dskygm