ArticleBioinformatics (Oxford, England)2016
Realizing privacy preserving genome-wide association studies.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- 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
- Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression.PLoS genetics · 2024Article
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Differential privacy in health research: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Privacy challenges and research opportunities for genomic data sharing.Nature genetics · 2020Review
- Privacy-preserving techniques of genomic data-a survey.Briefings in bioinformatics · 2019Review
- A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side.BMC genomics · 2019Article
- Protecting Genomic Data Privacy with Probabilistic Modeling.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Towards practical privacy-preserving genome-wide association study.BMC bioinformatics · 2018Article
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
- Secure genome-wide association analysis using multiparty computation.Nature biotechnology · 2018Article
- Efficient differentially private learning improves drug sensitivity prediction.Biology direct · 2018Article
- Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies.Bioinformatics (Oxford, England) · 2017Article
- Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations.Cell systems · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationAs genomics moves into the clinic, there has been much interest in using this medical data for research. At the same time the use of such data raises many privacy concerns. These circumstances have led to the development of various methods to perform genome-wide association studies (GWAS) on patient records while ensuring privacy. In particular, there has been growing interest in applying differentially private techniques to this challenge. Unfortunately, up until now all methods for finding high scoring SNPs in a differentially private manner have had major drawbacks in terms of either accuracy or computational efficiency.
resultsHere we overcome these limitations with a substantially modified version of the neighbor distance method for performing differentially private GWAS, and thus are able to produce a more viable mechanism. Specifically, we use input perturbation and an adaptive boundary method to overcome accuracy issues. We also design and implement a convex analysis based algorithm to calculate the neighbor distance for each SNP in constant time, overcoming the major computational bottleneck in the neighbor distance method. It is our hope that methods such as ours will pave the way for more widespread use of patient data in biomedical research. AVAILABILITY AND IMPLEMENTATION: A python implementation is available at http://groups.csail.mit.edu/cb/DiffPriv/ CONTACT: bab@csail.mit.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.