Identity inference of genomic data using long-range familial searches
By Yaniv Erlich, T. Shor, Itsik Pe'er, S. Carmi. Science, 2018
308 citations recorded by the source corpus.
Detecting familial matches Recent advances in DNA technology and companies that provide array-based testing have led to services that collect, share, and analyze volunteered genomic information. Privacy concerns have been raised, especially in light of the use of these services by law enforcement to identify suspects in criminal cases. Testing models of relatedness, Erlich et al. show that many individuals of European ancestry in the United States—even those that have not undergone genetic testing—can be identified on the basis of available genetic information. These results indicate a need for procedures to help maintain genetic privacy for individuals. Science, this issue p. 690 Genetic privacy is difficult to maintain in light of forensic searches of genetic genealogical databases. Consumer genomics databases have reached the scale of millions of individuals. Recently, law enforcement authorities have exploited some of these databases to identify suspects via distant familial relatives. Using genomic data of 1.28 million individuals tested with consumer genomics, we investigated the power of this technique. We project that about 60% of the searches for individuals of European descent will result in a third-cousin or closer match, which theoretically allows their identification using demographic identifiers. Moreover, the technique could implicate nearly any U.S. individual of European descent in the near future. We demonstrate that the technique can also identify research participants of a public sequencing project. On the basis of these results, we propose a potential mitigation strategy and policy implications for human subject research.
Subfields: Ecology, Genetics, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/7c66825af030f0ca73e2b186a88c8c8f5027e76a
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In Genetics
Citations in the corpus
- Privacy and personal data collection with information externalities (2019, background): “Erlich et al. (2018), for instance, show that only two percent of the population needs to have done a DNA test to identify nearly everyone else.”
- Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook (2024, unknown): “Other studies have shown that, even when sensitive patient data are anonymized, certain algorithms can still identify individual patients [189-191].”
- Privacy and artificial intelligence: challenges for protecting health information in a new era (2021, background): “” A 2018 study concluded that data collected by ancestry companies could be used to identify approximately 60% of Americans of European ancestry and that, in the near future, the percentage is likely to increase substantially [44].”
- Large language models in health care: Development, applications, and challenges (2023, background): “Moreover, despite anonymizing sensitive patient health information, some algorithms demonstrated the capability to reidentify these patients [49 – 51].”