Single-Cell Gene Expression Data Vulnerable to Privacy Breaches, Study Reveals

A study published recently in Cell has uncovered significant privacy vulnerabilities in single-cell RNA sequencing (scRNA-seq) datasets. Researchers from the New York Genome Center, Columbia University, and Brown University have demonstrated that individuals' genetic and physical trait information can be exposed through "linking attacks" on these datasets, challenging previous assumptions about their privacy risks.

Gamze Gürsoy, the study's corresponding author, explained, "Recently released population-scale single-cell datasets allowed us to approach the topic of privacy leakage and address the question of whether a hacker can work through the noise of single-cell data using publicly available information only to gain insight on a patient's genetic makeup and phenotypic traits and diseases."

The research team utilized data from a Lupus study and the OneK1K cohort to link individuals to their genetic and phenotypic profiles. They achieved this by comparing the data to publicly available bulk expression quantitative trait loci (eQTLs) and demonstrated even greater accuracy using cell-type specific eQTLs. Remarkably, they also showed that linking is possible without eQTL data by leveraging genetic and single-cell data from a smaller group to train a predictive model.

“We all know that gene expression patterns are influenced by genetic mutations, combinations of which are unique to each individual,” stated Conor Walker, first author on the study, adding, "We showed that by using genetic variants and single-cell RNA-Seq data from one cohort, we can identify positions that can be predicted in other studies, relying solely on the single-cell expression data from those studies. This approach allows the retrieval of genetic information that participants in unrelated studies never consented to sharing."

The study's findings highlight a significant privacy concern, as the method can be applied across different populations and even between healthy and diseased datasets. Dr. Gürsoy emphasized, "The ability to leverage data generated in a different lab and even processed with a different method, to then use it to link individuals in a completely different anonymous dataset, is rather striking and highlights a real privacy issue for single-cell data."

This research aims to inform future study designs, quantify risks associated with data release, and shape more comprehensive consent policies for single-cell data donors. It also underscores the need for laws and legislation to prevent the misuse of this sensitive information.