The growing use of synthetic genomic data promises broader data access but raises unresolved concerns about privacy risk. We introduce PRISM–G, a model–agnostic framework that summarizes privacy exposure of synthetic genomes across three complementary components: (i) a proximity view that asks whether synthetic individuals lie unusually close to real genomes in genetic-coordinate space; (ii) a kinship view that detects replay of familial or population-structure patterns beyond what is expected by chance; and (iii) a trait-linked view that captures exposure through rare variants and simple membership-inference signals. Each component yields a normalized risk score and a risk-averse aggregation maps these to a 0—100 PRISM–G score. We evaluated PRISM–G on synthetic cohorts generated by a generative adversarial network (GAN), a restricted Boltzmann machine (RBM), and a logic–based SAT-solver (Genomator). Our results show that privacy vulnerabilities concentrate along different axes across models and marker densities, underscoring that a single privacy-based similarity metric is insufficient.
Rojo et al. (Fri,) studied this question.