Protecting Genomic Data Privacy with Probabilistic Modeling.

Sean Simmons, Bonnie Berger, Cenk Sahinalp

Abstract read

In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sean SimmonsStanley Center, Broad Institute, Cambriadge, MA 02142, USA, ssimmons@broadinstitute.org.

Bonnie BergerCSAIL and Department of Mathematics, MIT, Cambriadge, MA 02142, USA.

Cenk SahinalpDepartment of Computer Science, Indiana University, Bloomington, Indiana 47405, USA.

Funding

Compressive genomics for large omics data sets: Algorithms applications & toolsR01GM108348 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BERGER, BONNIE · 2013 to 2019

$2.1M

NIGMS NIH HHS R01 GM108348

6 · The paper itself

Abstract

The proliferation of sequencing technologies in biomedical research has raised many new privacy concerns. These include concerns over the publication of aggregate data at a genomic scale (e.g. minor allele frequencies, regression coefficients). Methods such as differential privacy can overcome these concerns by providing strong privacy guarantees, but come at the cost of greatly perturbing the results of the analysis of interest. Here we investigate an alternative approach for achieving privacy-preserving aggregate genomic data sharing without the high cost to accuracy of differentially private methods. In particular, we demonstrate how other ideas from the statistical disclosure control literature (in particular, the idea of disclosure risk) can be applied to aggregate data to help ensure privacy. This is achieved by combining minimal amounts of perturbation with Bayesian statistics and Markov Chain Monte Carlo techniques. We test our technique on a GWAS dataset to demonstrate its utility in practice.

Indexed as

Genetic PrivacyBayes TheoremComputational BiologyDisclosureGene FrequencyGenome-Wide Association StudyGenomicsHumansInformation DisseminationMarkov ChainsModels, StatisticalMonte Carlo MethodPolymorphism, Single NucleotideGenomic PrivacyGWASMCMC

Identifiers

PMID30963078

PMCPMC6447070

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.