ArticleBMC medical ethics2010
Caught you: threats to confidentiality due to the public release of large-scale genetic data sets.
Article in BMC medical ethics, 2010. 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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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.
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Who cites it
14 citing papers in PubMed.
- Blockchain Technology in Digital Health and Medical Technologies.Blockchain in healthcare today · 2025Article
- Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.Frontiers in big data · 2024Article
- Italian public's views on sharing genetic information and medical information: findings from the 'Your DNA, Your Say' study.Wellcome open research · 2021Article
- Challenges and Legal Gaps of Genetic Profiling in the Era of Big Data.Frontiers in big data · 2019Article
- A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock.Frontiers in genetics · 2019Review
- Society and personal genome data.Human molecular genetics · 2018Review
- The challenges of the expanded availability of genomic information: an agenda-setting paper.Journal of community genetics · 2018Article
- Legal issues in governing genetic biobanks: the Italian framework as a case study for the implications for citizen's health through public-private initiatives.Journal of community genetics · 2018Article
- The risk of re-identification versus the need to identify individuals in rare disease research.European journal of human genetics : EJHG · 2016Article
- Informants a potential threat to confidentiality in small studies.Medicine, health care, and philosophy · 2015Article
- Reporting ethics committee approval in public administration research.Science and engineering ethics · 2014Article
- Policy recommendations for addressing privacy challenges associated with cell-based research and interventions.BMC medical ethics · 2014Article
- Governing biological material at the intersection of care and research: the use of dried blood spots for biobanking.Croatian medical journal · 2012Article
- Methods for the de-identification of electronic health records for genomic research.Genome medicine · 2011Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge-scale genetic data sets are frequently shared with other research groups and even released on the Internet to allow for secondary analysis. Study participants are usually not informed about such data sharing because data sets are assumed to be anonymous after stripping off personal identifiers. DISCUSSION: The assumption of anonymity of genetic data sets, however, is tenuous because genetic data are intrinsically self-identifying. Two types of re-identification are possible: the "Netflix" type and the "profiling" type. The "Netflix" type needs another small genetic data set, usually with less than 100 SNPs but including a personal identifier. This second data set might originate from another clinical examination, a study of leftover samples or forensic testing. When merged to the primary, unidentified set it will re-identify all samples of that individual. Even with no second data set at hand, a "profiling" strategy can be developed to extract as much information as possible from a sample collection. Starting with the identification of ethnic subgroups along with predictions of body characteristics and diseases, the asthma kids case as a real-life example is used to illustrate that approach. SUMMARY: Depending on the degree of supplemental information, there is a good chance that at least a few individuals can be identified from an anonymized data set. Any re-identification, however, may potentially harm study participants because it will release individual genetic disease risks to the public.
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Registered trials
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