Privacy-Preserving Collaborative Population Stratification with Dynamic Algorithm and Hyperparameter Selection

We present a privacy-preserving selection layer for collaborative population stratification under $ϵ$ -local differential privacy (LDP). Rather than fixing a single pipeline (e.g., PCA+K-Means with preset $K$ ), our framework lets parties choose among three DP pipelines: PCA→Noise, Noise→PCA, and Noise-Only, according to their resources, and has an honest-but-curious server aggregate only DP shares to automatically select the clustering algorithm (K-Means, GMM, or Hierarchical) and $K$ that maximize internal metrics (Silhouette, Calinski-Harabasz, Davies-Bouldin). Because selection operates on DP data, it adds no further privacy loss. On openSNP (942 samples, 28,396 SNPs), the PCA-augmented pipelines yield higher utility and substantially lower communication and runtime than Noise-Only, and the recommended configuration consistently outperforms fixed baselines. Membership-inference attack power remains markedly lower for PCA-based pipelines across privacy budgets $ϵ$ . In this paper, experiments are limited to two collaborating parties; extensions to multi-site collaboration are left for future work.