ArticleProceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium2022
Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies.
Article in Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 14 citations in OpenAlex.
- Privacy-preserving verification of preprocessing in federated learning for genomic data.JAMIA open · 2026Article
- PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2026Article
- A reinforcement learning-based approach for dynamic privacy protection in genomic data sharing beacons.Genome biology · 2025Article
- Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Genomic privacy and security in the era of artificial intelligence and quantum computing.Discover computing · 2025Review
- Privacy-Preserving Verification of ML Preprocessing via Model Behavior Indicators.IEEE transactions on privacy · 2025Article
- Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors.bioRxiv : the preprint server for biology · 2024Article
- Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.Briefings in bioinformatics · 2024Review
- Privacy-Preserving Fingerprinting Against Collusion and Correlation Threats in Genomic Data.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 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
- Privacy-preserving federated genome-wide association studies via dynamic sampling.Bioinformatics (Oxford, England) · 2023Article
- Privacy preserving identification of population stratification for collaborative genomic research.Bioinformatics (Oxford, England) · 2023Article
- Robust fingerprinting of genomic databases.Bioinformatics (Oxford, England) · 2022Article
- Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. In this work, we propose a framework that verifies the correctness of the aggregate statistics obtained as a result of a genome-wide association study (GWAS) conducted by a researcher while protecting individuals' privacy in the researcher's dataset. In GWAS, the goal of the researcher is to identify highly associated point mutations (variants) with a given phenotype. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another GWAS (conducted using publicly available datasets) to distinguish between correct statistics and incorrect ones. For evaluation, we use real genomic data and show that the correctness of the workflow output can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results.
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What OpenQuestion holds
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.