Identifying Personal Genomes by Surname Inference

By M. Gymrek, A. McGuire, David Golan, Eran Halperin, Yaniv Erlich. Science, 2013

1,122 citations recorded by the source corpus.

Subfields: Genetics, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/c5a4e5024a90aeae16298d8228d67dd9c9182080

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Blake Murdoch
2. Itsik Pe'er
3. Seunghak Lee
4. Wen-Yun Yang
5. Christoph Lippert
6. Chloé-Agathe Azencott
7. Chao Xie

In Pediatrics, Perinatology and Child Health

1. Somesh Jha
2. Matt Fredrikson
3. Chloé-Agathe Azencott

Citations in the corpus

• Machine learning and genomics: precision medicine versus patient privacy (2018, background): “To give a few examples, [20] showed how to predict the values of a thousand SNPs from easily available gene expression data, and how to re-identify individuals in SNP databases using these SNPs only; [21] identified people’s surnames from profiling short tandem repeats on the Y chromosome and querying”
• Private Predictive Analysis on Encrypted Medical Data (2014, background): “However, privacy concerns, even when the data is anonymized, need to be weighed in the balance (see [4] for a recent study on re-identification of data in this context where certain high-profile individuals are re-identified).”
• The Unintended Consequences of Overfitting: Training Data Inference Attacks (2017, background): “A number of previous studies [40, 19, 20, 41, 42, 43] have looked into membership attacks from statistics commonly published in genome-wide association studies (GWAS).”
• 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].”
• No major flaws in “Identification of individuals by trait prediction using whole-genome sequencing data” (2017, background): “The genome also provides information about demographic variables, such as gender, ethnicity, and age, plus others not treated in this study, such as surnames [Gymrek et al., 2013].”
• Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting (2017, result): “A number of previous studies [21], [22], [47], [48], [49], [50] have looked into membership attacks from statistics commonly published in genome-wide association studies (GWAS).”
• Identity inference of genomic data using long-range familial searches (2018, methodology): “We selected a female from the CEU cohort in Utah, whose husband has been identified using surname inference (17).”
• Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook (2024, neutral): “Other studies have shown that, even when sensitive patient data are anonymized, certain algorithms can still identify individual patients [189-191].”
• Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, neutral): “However, accessing this growing pool of data poses major privacy concerns for individuals (Gymrek et al., 2013; Malin et al., 2013; Murphy et al., 2011; Nyholt et al., 2009).”
• Privacy and artificial intelligence: challenges for protecting health information in a new era (2021, background): “And this is true even if the information has been anonymized and scrubbed of all identifiers [42].”