ArticleNature methods2016
Quantification of private information leakage from phenotype-genotype data: linking attacks.
Article in Nature methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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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
59 citing papers in PubMed, 1 synthesis or guideline pooled it, 138 citations in OpenAlex.
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- NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.Bioinformatics (Oxford, England) · 2026Article
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference.Statistics in medicine · 2026Review
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- FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data.Genome biology · 2025Article
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- Proxy panels enable privacy-aware outsourcing of genotype imputation.Genome research · 2025Article
- Secure and federated quantitative trait loci mapping with privateQTL.Cell genomics · 2025Article
- A framework for privacy-preserving similarity search of massive multi-party genomic data.Frontiers in plant science · 2025Article
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- Privacy of single-cell gene expression data.Patterns (New York, N.Y.) · 2024Article
- Synthetic data for privacy-preserving clinical risk prediction.Scientific reports · 2024Article
- Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.Journal of biomedical informatics · 2024Article
- Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Studies on genomic privacy have traditionally focused on identifying individuals using DNA variants. In contrast, molecular phenotype data, such as gene expression levels, are generally assumed to be free of such identifying information. Although there is no explicit genotypic information in phenotype data, adversaries can statistically link phenotypes to genotypes using publicly available genotype-phenotype correlations such as expression quantitative trait loci (eQTLs). This linking can be accurate when high-dimensional data (i.e., many expression levels) are used, and the resulting links can then reveal sensitive information (for example, the fact that an individual has cancer). Here we develop frameworks for quantifying the leakage of characterizing information from phenotype data sets. These frameworks can be used to estimate the leakage from large data sets before release. We also present a general three-step procedure for practically instantiating linking attacks and a specific attack using outlier gene expression levels that is simple yet accurate. Finally, we describe the effectiveness of this outlier attack under different scenarios.
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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.