ArticleJMIR bioinformatics and biotechnology2024
Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review.
Article in JMIR bioinformatics and biotechnology, 2024. 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.
- PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome data.Bioinformatics (Oxford, England) · 2026Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- WiNGS-API: a federated genome/phenome data sharing platform enabling gene discovery and variant classification for rare diseases.Genome medicine · 2026Article
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- The European Health Data Space and biobanking in Europe: synergies, tensions and the future governance of data-driven health research.Frontiers in genetics · 2026Review
- Rethinking scale in AI-driven genomic medicine - The role of small biobanks.Frontiers in genetics · 2026Article
- Advancing Real-World Evidence Generation: Growth and Lessons Learned from the Early Years of Cosmos.ACI open · 2026Review
- Federated, governed, and interoperable? The emerging architecture of public human genomic data infrastructures: a European perspective.Frontiers in genetics · 2026Review
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Power and Limitations of Inferring Genetic Ancestry.Annals of human genetics · 2025Review
- Balancing ethical data sharing and open science for reproducible research in biomedical data science.Cell reports. Medicine · 2025Article
- Advancing artificial intelligence ethics in health and genomics: lessons from a public survey in South Korea.Frontiers in genetics · 2025Article
- Can Open Science Advance Health Justice? Genomic Research Dissemination in the Evolving Data-Sharing Landscape.The Hastings Center report · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation.
objectiveThis study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets.
methodsWe conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success.
resultsFrom our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants.
conclusionsOn the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.
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