Privacy-preserving GWAS analysis on federated genomic datasets

Constable, Scott D; Tang, Yuzhe; Wang, Shuang; Jiang, Xiaoqian; Chapin, Steve J.

doi:10.1186/1472-6947-15-s5-s2

Cited by 53 publications

(58 citation statements)

References 36 publications

(39 reference statements)

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“…[55] that secret sharing techniques are more efficient than encryption-based techniques for privacy-preserving data mining with respect to communication, computation and storage cost. Secure multi-party computation-based secret sharing techniques have been used to protect privacy in evaluating count and ranked queries [39] and in GWAS (Genome-Wide Association Studies) [32], [51], [58]. In Ref.…”

Section: Secret-sharing Techniquesmentioning

confidence: 99%

Towards Privacy-preserving Authenticated Disease Risk Queries

Mozumder

Das²,

Hashem

et al. 2019

Journal of Information Processing

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Recent improvement in genomic research is paving the way towards significant progress in diagnosis and treatment of diseases. A disease risk query returns the probability of a patient to develop a particular disease based on her genomic and clinical data. Despite various innovative prospects, frequent and ubiquitous usage of genomic data in medical tests and personalized medicine may cause various privacy threats like genetic discrimination, exposure of susceptibility to diseases, and revelation of genomic data of relatives. Another major concern is on ensuring the reliability of the genome data and the correctness of the computed disease risk, which is known as authentication. We develop a novel secret sharing approach to protect privacy of sensitive genomic and clinical data, disease markers, disease name, and the query answer while ensuring authenticated result of the disease risk query. In addition, we discuss the applicability of our approach in the field of personalized medicine. We perform a comprehensive security analysis for our system. Experiments with real datasets show that our approach for authenticated disease risk queries achieves a high level of privacy with reduced processing and storage overhead.

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Section: Secret-sharing Techniquesmentioning

confidence: 99%

Towards Privacy-preserving Authenticated Disease Risk Queries

Mozumder

Das²,

Hashem

et al. 2019

Journal of Information Processing

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“…Multiple garbled circuit methods have been proposed to analyze genomic data, in particular for computing similarities between sequences [52,53] or for case-control GWAS studies [54]. However, these approaches are limited to two-party computations, meaning that they yet have to be adapted to the case where more than two partners want to collaborate on a federated genomic study.…”

Section: Secure Multi-party Computationmentioning

confidence: 99%

Machine learning and genomics: precision medicine versus patient privacy

Azencott

2018

Phil. Trans. R. Soc. A.

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Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

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“…However, so far this approach appears to be rather slow and of limited use, since only data provided by relatively few participating patients can be processed. For example, in [14] the number of participants is restricted to 32 768, the precision is unsatisfying due to 16-bit floating point arithmetic, and it takes ∼22 min to compute the χ 2 -test on ∼9 000 inputs.…”

Section: Motivationmentioning

confidence: 99%

Large-Scale Privacy-Preserving Statistical Computations for Distributed Genome-Wide Association Studies

Tkachenko

Weinert

Schneider

et al. 2018

Proceedings of the 2018 on Asia Conference on Computer and Communications Security

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We present privacy-preserving solutions for Genome-Wide Association Studies (GWAS) based on Secure Multi-Party Computation (SMPC). Using SMPC, we protect the privacy of patients when medical institutes collaborate for computing statistics on genomic data in a distributed fashion. Previous solutions for this task lack efficiency and/or use inadequate algorithms that are of limited practical value. Concretely, we optimize and implement multiple algorithms for the χ 2-, G-, and P-test in the ABY framework (Demmler et al., NDSS'15) and evaluate them in a distributed GWAS scenario. Statistical tests generally require advanced mathematical operations. For operations that cannot be calculated in integer arithmetic, we make use of the existing IEEE 754 floating point arithmetic implementation in ABY (Demmler et al., CCS'15). To improve performance, we extend the mixed-protocol capabilities of ABY by optimizing and implementing the integer to floating point conversion protocols of Aliasgari et al. (NDSS'13), which may be of independent interest. Furthermore, we consider extended contingency tables for the χ 2-and G-test that use codeword counts instead of counts for only two alleles, thereby allowing for advanced, realistic analyses. Finally, we consider an outsourcing scenario where two non-colluding semi-trusted third parties process secret-shared input data from multiple institutes. Our extensive evaluation shows, compared to the prior art of Constable et al. (BMC Medical Informatics and Decision Making'15), an improved run-time efficiency of the χ 2-test by up to factor 37x. We additionally demonstrate practicality in scenarios with millions of participants and hundreds of collaborating institutes. CCS CONCEPTS • Security and privacy → Privacy-preserving protocols; Management and querying of encrypted data; Privacy protections;

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Privacy-preserving GWAS analysis on federated genomic datasets

Cited by 53 publications

References 36 publications

Towards Privacy-preserving Authenticated Disease Risk Queries

Towards Privacy-preserving Authenticated Disease Risk Queries

Machine learning and genomics: precision medicine versus patient privacy

Large-Scale Privacy-Preserving Statistical Computations for Distributed Genome-Wide Association Studies

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