Private information leakage from single-cell count matrices.

ABSTRACT: The increase in publicly available human single-cell datasets, encompassing millions of cells from many donors, has significantly enhanced our understanding of complex biological processes. However, the accessibility of these datasets raises significant privacy concerns. Due to the inherent noise in single-cell measurements and the scarcity of population-scale single-cell datasets, recent private information quantification studies have focused on bulk gene expression data sharing. To address this gap, we demonstrate that individuals in single-cell gene expression datasets are vulnerable to linking attacks, where attackers can infer their sensitive phenotypic information using publicly available tissue or cell-type-specific expression quantitative trait loci (eQTLs) information. We further

SUBMITTER: Walker CR 

PROVIDER: S-EPMC11568916 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature