ArticleJournal of biomedical informatics2014
Scalable privacy-preserving data sharing methodology for genome-wide association studies.
Article in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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.
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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.
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Who cites it
29 citing papers in PubMed.
- PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2026Article
- Privacy-preserving framework for genomic computations via multi-key homomorphic encryption.Bioinformatics (Oxford, England) · 2025Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.Science advances · 2024Article
- Ensuring Trust in Genomics Research.... IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications : (TPS-ISA ...). IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications · 2023Article
- A Survey on Differential Privacy for Medical Data Analysis.Annals of data science · 2023Review
- Tuning Privacy-Utility Tradeoff in Genomic Studies Using Selective SNP Hiding.Proceedings of the ... Asia-Pacific bioinformatics conference · 2023Article
- Genomic Data Sharing under Dependent Local Differential Privacy.CODASPY : proceedings of the ... ACM conference on data and application security and privacy. ACM Conference on Data and Application Security & Privacy · 2022Article
- Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2022Article
- Differential privacy in health research: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Privacy-preserving chi-squared test of independence for small samples.BioData mining · 2021Article
- Genome Reconstruction Attacks Against Genomic Data-Sharing Beacons.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2021Article
- Preserving Genomic Privacy via Selective Sharing.Proceedings of the ACM Workshop on Privacy in the Electronic Society. ACM Workshop on Privacy in the Electronic Society · 2020Article
- Inference attacks against differentially private query results from genomic datasets including dependent tuples.Bioinformatics (Oxford, England) · 2020Article
- Privacy challenges and research opportunities for genomic data sharing.Nature genetics · 2020Review
- Big Data Privacy in Biomedical Research.IEEE transactions on big data · 2020Article
- 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
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
- Efficient differentially private learning improves drug sensitivity prediction.Biology direct · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The protection of privacy of individual-level information in genome-wide association study (GWAS) databases has been a major concern of researchers following the publication of "an attack" on GWAS data by Homer et al. (2008). Traditional statistical methods for confidentiality and privacy protection of statistical databases do not scale well to deal with GWAS data, especially in terms of guarantees regarding protection from linkage to external information. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach that provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information, although the guarantees may come at a serious price in terms of data utility. Building on such notions, Uhler et al. (2013) proposed new methods to release aggregate GWAS data without compromising an individual's privacy. We extend the methods developed in Uhler et al. (2013) for releasing differentially-private χ(2)-statistics by allowing for arbitrary number of cases and controls, and for releasing differentially-private allelic test statistics. We also provide a new interpretation by assuming the controls' data are known, which is a realistic assumption because some GWAS use publicly available data as controls. We assess the performance of the proposed methods through a risk-utility analysis on a real data set consisting of DNA samples collected by the Wellcome Trust Case Control Consortium and compare the methods with the differentially-private release mechanism proposed by Johnson and Shmatikov (2013).
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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.