Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption.

BMC Medical Genomics
Sergiu CarpovJuan Ramón Troncoso-Pastoriza

Abstract

Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithms to be securely deployed in the cloud because operations on encrypted genomic databases are conducted without revealing any individual genomes. Methods for secure computation have shown significant performance improvements over the last several years. However, it is still challenging to apply them on large biomedical datasets. The HE Track of iDash 2018 competition focused on solving an important problem in practical machine learning scenarios, where a data analyst that has trained a regression model (both linear and logistic) with a certain set of features, attempts to find all features in an encrypted database that will improve the quality of the model. Our solution is based on the hybrid framework Chimera that allows for switching between different families of fully homomorphic schemes, namely TFHE and HEAAN. Our solution is one of the finalist of Track 2 of iDash 2018 competition. Among the submitted solutions, ours is the only bootstrapped approach that can be applied for different...Continue Reading

References

Jan 19, 2013·Science·Melissa GymrekYaniv Erlich
Aug 19, 2017·Science·Karthik A JagadeeshGill Bejerano
Sep 7, 2017·Proceedings of the National Academy of Sciences of the United States of America·Christoph LippertJ Craig Venter
Jul 12, 2018·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Md Nazmus SadatShuang Wang
Oct 13, 2018·BMC Medical Genomics·Andrey KimJung Hee Cheon
Oct 13, 2018·BMC Medical Genomics·Hao ChenKristin Lauter
Oct 13, 2018·BMC Medical Genomics·Sergiu Carpov, Thibaud Tortech
Oct 13, 2018·BMC Medical Genomics·Charlotte Bonte, Frederik Vercauteren

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Methods Mentioned

BETA
iDASH

Software Mentioned

Chimera
TLWE
TFHE
Torus
TRLWE
iDASH

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