Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)

Shi, Haoyi; Jiang, Chao; Dai, Wenrui; Jiang, Xiaoqian; Tang, Yuzhe; Ohno-Machado, Lucila; Wang, Shuang

doi:10.1186/s12911-016-0316-1

Cited by 46 publications

(31 citation statements)

References 46 publications

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“…Many HME-based applications have been studied for safeguarding linear classification [ 23 ], predictive analysis on encrypted medical data [ 24 ], genetic association studies [ 25 , 26 ], Edit distance computation [ 27 ], GWAS study using exact logistic regression [ 28 ]. Secure multiparty computation (SMC) is another widely adopted technique for securing genomic data analysis, such as secure multiparty GWAS [ 29 – 33 ], secure distributed regression model learning [ 34 ] and so on. However, the high computational complexity of the existing HME and SMC solutions plague their practical adoption over the large-scale genomic data.…”

Section: Introductionmentioning

confidence: 99%

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

et al. 2017

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BackgroundAdvances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data.MethodsWe present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing.ResultsWe compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation.ConclusionsThe proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

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Section: Introductionmentioning

confidence: 99%

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

et al. 2017

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“…The former developed simple association tests including Cochran-Armitage and chi-square (χ 2 ) and the latter implemented only Cochran-Armitage test. Shi et al 19 presented a secure SMPC-based logistic regression framework for GWAS. These frameworks inherit the limitations of SMPC itself: They follow the paradigm of "move data to computation", where they put the processing burden on a few computing parties.…”

Section: Introductionmentioning

confidence: 99%

sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies

Nasirigerdeh

Torkzadehmahani

Matschinske

et al. 2020

Preprint

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ABSTRACTGenome-wide association studies (GWAS) have been widely used to unravel connections between genetic variants and diseases. Larger sample sizes in GWAS can lead to discovering more associations and more accurate genetic predictors. However, sharing and combining distributed genomic data to increase the sample size is often challenging or even impossible due to privacy concerns and privacy protection laws such as the GDPR. While meta-analysis has been established as an effective approach to combine summary statistics of several GWAS, its accuracy can be attenuated in the presence of cross-study heterogeneity. Here, we present sPLINK (safe PLINK), a user-friendly tool, which performs federated GWAS on distributed datasets while preserving the privacy of data and the accuracy of the results. sPLINK neither exchanges raw data nor does it rely on summary statistics. Instead, it performs model training in a federated manner, communicating only model parameters between cohorts and a central server. We verify that the federated results from sPLINK are the same as those from aggregated analyses conducted with PLINK. We demonstrate that sPLINK is robust against heterogeneous data (phenotype and confounding factors) distributions across cohorts while existing meta-analysis tools considerably lose accuracy in such scenarios. We also show that sPLINK achieves practical runtime, in order of minutes or hours, and acceptable network bandwidth consumption for chi-square and linear/logistic regression tests. Federated analysis with sPLINK, thus, has the potential to replace meta-analysis as the gold standard for collaborative GWAS. The user-friendly, readily usable sPLINK tool is available at https://exbio.wzw.tum.de/splink.

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“…genetic association, logistic regression, genomic medicine). Secure multi-party computation protocols can also be used to provide private genomic data analysis [ 21 – 23 ]. The main issue of these solutions is the performance bottleneck when applied to large-scale genomic data computations.…”

Section: Introductionmentioning

confidence: 99%

Secure top most significant genome variants search: iDASH 2017 competition

Carpov

Tortech

2018

BMC Med Genomics

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BackgroundOne of the 3 tracks of iDASH Privacy & Security Workshop 2017 competition was to execute a whole genome variants search on private genomic data. Particularly, the search application was to find the top most significant SNPs (Single-Nucleotide Polymorphisms) in a database of genome records labeled with control or case. In this paper we discuss the solution submitted by our team to this competition.MethodsPrivacy and confidentiality of genome data had to be ensured using Intel SGX enclaves. The typical use-case of this application is the multi-party computation (each party possessing one or several genome records) of the SNPs which statistically differentiate control and case genome datasets.ResultsOur solution consists of two applications: (i) compress and encrypt genome files and (ii) perform genome processing (top most important SNPs search). We have opted for a horizontal treatment of genome records and heavily used parallel processing. Rust programming language was employed to develop both applications.ConclusionsExecution performance of the processing applications scales well and very good performance metrics are obtained. Contest organizers selected it as the best submission amongst other received competition entries and our team was awarded the first prize on this track.

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Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)

Cited by 46 publications

References 46 publications

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

sPLINK: A Federated, Privacy-Preserving Tool as a Robust Alternative to Meta-Analysis in Genome-Wide Association Studies

Secure top most significant genome variants search: iDASH 2017 competition

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