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GenoPPML - a framework for genomic privacy-preserving machine learning
[article]
2021
IACR Cryptology ePrint Archive
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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
2022
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
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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. ...
Genoppml–a 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