ArticleBMC bioinformatics2018
Towards practical privacy-preserving genome-wide association study.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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.
Who cites it
13 citing papers in PubMed.
- PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies.Nature communications · 2025Article
- Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption.Genome research · 2024Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.Briefings in bioinformatics · 2024Review
- EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation.BMC bioinformatics · 2022Article
- SPIKE: secure and private investigation of the kidney exchange problem.BMC medical informatics and decision making · 2022Article
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Privacy-preserving approximate GWAS computation based on homomorphic encryption.BMC medical genomics · 2020Article
- Semi-Parallel logistic regression for GWAS on encrypted data.BMC medical genomics · 2020Article
- Private Genomes and Public SNPs: Homomorphic Encryption of Genotypes and Phenotypes for Shared Quantitative Genetics.Genetics · 2020Article
- Private Hospital Workflow Optimization via Secure k-Means Clustering.Journal of medical systems · 2019Article
- Informed Consent in Biomedical Research.Computational and structural biotechnology journal · 2019Review
- Towards practical privacy-preserving genome-wide association study.BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe deployment of Genome-wide association studies (GWASs) requires genomic information of a large population to produce reliable results. This raises significant privacy concerns, making people hesitate to contribute their genetic information to such studies.
resultsWe propose two provably secure solutions to address this challenge: (1) a somewhat homomorphic encryption (HE) approach, and (2) a secure multiparty computation (MPC) approach. Unlike previous work, our approach does not rely on adding noise to the input data, nor does it reveal any information about the patients. Our protocols aim to prevent data breaches by calculating the χ
conclusionsWe show that full-scale privacy-preserving GWAS is practical, as long as the statistics can be computed by low degree polynomials. Our implementations demonstrated that both approaches are efficient. The secure multiparty computation technique completes its execution in approximately 2 ms for data contributed by one million subjects.
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Registered trials
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.