Dataset Information

Privacy-preserving microbiome analysis using secure computation.

ABSTRACT:

Motivation

Developing targeted therapeutics and identifying biomarkers relies on large amounts of research participant data. Beyond human DNA, scientists now investigate the DNA of micro-organisms inhabiting the human body. Recent work shows that an individual's collection of microbial DNA consistently identifies that person and could be used to link a real-world identity to a sensitive attribute in a research dataset. Unfortunately, the current suite of DNA-specific privacy-preserving analysis tools does not meet the requirements for microbiome sequencing studies.

Results

To address privacy concerns around microbiome sequencing, we implement metagenomic analyses using secure computation. Our implementation allows comparative analysis over combined data without revealing the feature counts for any individual sample. We focus on three analyses and perform an evaluation on datasets currently used by the microbiome research community. We use our implementation to simulate sharing data between four policy-domains. Additionally, we describe an application of our implementation for patients to combine data that allows drug developers to query against and compensate patients for the analysis.

Availability and implementation

The software is freely available for download at: http://cbcb.umd.edu/?hcorrada/projects/secureseq.html

Supplementary information

Supplementary data are available at Bioinformatics online.

Contact

hcorrada@umiacs.umd.edu.

SUBMITTER: Wagner J

PROVIDER: S-EPMC4908319 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Publications

Privacy-preserving microbiome analysis using secure computation.

Wagner Justin J Paulson Joseph N JN Wang Xiao X Bhattacharjee Bobby B Corrada Bravo Héctor H

Bioinformatics (Oxford, England) 20160211 12

<h4>Motivation</h4>Developing targeted therapeutics and identifying biomarkers relies on large amounts of research participant data. Beyond human DNA, scientists now investigate the DNA of micro-organisms inhabiting the human body. Recent work shows that an individual's collection of microbial DNA consistently identifies that person and could be used to link a real-world identity to a sensitive attribute in a research dataset. Unfortunately, the current suite of DNA-specific privacy-preserving a ...[more]

PMID: 26873931

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Project description:BackgroundPatient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from different institutions are pooled and evaluated together. Thus, the goals of data utilization and data protection are in conflict. Secure multiparty computation (SMPC) solves this conflict because it allows direct computation on distributed proprietary data-held by different data owners-in a secure way without exchanging private data.ObjectiveThe objective of this work was to provide a proof-of-principle of secure and privacy-preserving multicentric computation by SMPC with real-patient data over the free internet. A privacy-preserving log-rank test for the Kaplan-Meier estimator was implemented and tested in both an experimental setting and a real-world setting between two university hospitals.MethodsThe domain of survival analysis is particularly relevant in clinical research. For the Kaplan-Meier estimator, we provided a secure version of the log-rank test. It was based on the SMPC realization SPDZ and implemented via the FRESCO framework in Java. The complexity of the algorithm was explored both for synthetic data and for real-patient data in a proof-of-principle over the internet between two clinical institutions located in Munich and Berlin, Germany.ResultsWe obtained a functional realization of an SMPC-based log-rank evaluation. This implementation was assessed with respect to performance and scaling behavior. We showed that network latency strongly influences execution time of our solution. Furthermore, we identified a lower bound of 2 Mbit/s for the transmission rate that has to be fulfilled for unimpeded communication. In contrast, performance of the participating parties have comparatively low influence on execution speed, since the peer-side processing is parallelized and the computational time only constitutes 30% to 50% even with optimal network settings. In the real-world setting, our computation between three parties over the internet, processing 100 items each, took approximately 20 minutes.ConclusionsWe showed that SMPC is applicable in the medical domain. A secure version of commonly used evaluation methods for clinical studies is possible with current implementations of SMPC. Furthermore, we infer that its application is practically feasible in terms of execution time.

Dataset Information

Privacy-preserving microbiome analysis using secure computation.

Motivation

Results

Availability and implementation

Supplementary information

Contact

Publications

Privacy-preserving microbiome analysis using secure computation.

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