A joint optimization-based novel attack for genomic beacon reconstruction

Genomic data sharing has become an essential component of biomedical research, enabling large-scale collaborations and accelerating discoveries in human genetics. To balance the need for accessibility with privacy concerns, several controlled-access mechanisms have been proposed, including genomic beacons. Genomic beacons answer simple presence/absence queries about specific genetic variants. However, prior work has demonstrated that beacons remain vulnerable to genome reconstruction attacks, where an adversary can recover large portions of participants’ genomes using summary statistics. Building on insights from prior reconstruction attacks, we introduce an approach that unifies SNP correlation and allele frequency alignment objectives within a single-stage joint optimization framework. Unlike earlier two-stage methods that alternately minimized correlation and frequency losses, our joint formulation improves both reconstruction accuracy and computational effeciency, achieving an average F1-score of 71.4%, a 1.4 percentage point improvement over the state-of-the-art method and substantially outperforming the baseline approach (45%), while reducing the runtime for reconstructing 2000 SNPs across 100 individuals from 10 to 7.4 h, marking a 26% decrease in computational cost. These results underscore the pressing need for more robust privacy-preserving mechanisms in genomic beacon protocols.