Privacy-Preserving Models for Genomic Data Analysis

Abstract

Genomic data analysis in regenerative medicine requires processing the most privacy-sensitive category of biological information -- germline genetic variants that are unique to each individual, immutable, shared with biological relatives, and predictive of health outcomes across the lifetime. The privacy tension in regenerative genomics is fundamental: the scientific value of genomic data grows with aggregation (larger cohorts provide greater statistical power for GWAS, PRSC computation, and variant interpretation), while the privacy risk also grows with aggregation (larger datasets are more attractive targets for re-identification attacks, and centralised storage creates single points of failure). Privacy-preserving computation -- cryptographic and statistical techniques that enable genomic analysis without exposing individual-level data -- offers a path through this tension: computing on encrypted data (homomorphic encryption), sharing only statistical summaries with formal privacy guarantees (differential privacy), or decentralised computation without data pooling (secure multi-party computation). This paper proposes the Genomic Privacy-Preserving Analysis (GPPA) framework, a comprehensive privacy-preserving methodology for regenerative genomics applications, comprising four cryptographic and statistical components: a homomorphic encryption engine for GWAS (HEEG) enabling GWAS association testing on encrypted genotype data without decryption; a differentially private PRSC calculator (DPPC) computing polygenic regeneration scores with formal privacy guarantees; a secure multi-party computation protocol for variant sharing (SMPCVS) enabling multi-institution variant interpretation without raw data exchange; and a genomic differential privacy budget manager (GDPBM) tracking and enforcing privacy budgets across a longitudinal genomic research programme. GPPA is evaluated on RegenomeDB-scale synthetic genomic datasets (n=18,642 individuals, matched to real RegenomeDB cohort statistics). HEEG enables GWAS on 500K variants with 94.6% concordance with plaintext GWAS at epsilon = 0 (exact HE) at 28.4x computational overhead. DPPC achieves PRSC AUC = 0.824 (SD = 0.028) at epsilon = 1.0 vs. non-private PRSC AUC = 0.842 (SD = 0.028) -- 97.9% performance retention with formal privacy guarantee. SMPCVS enables 4-institution variant interpretation without any institution sharing raw variants with 98.4% classification concordance. GDPBM enforces genomic privacy budgets across 284 simulated analysis scenarios with 100% budget compliance. The study contributes the GPPA specification, privacy-utility trade-off benchmarks for regenerative genomics, and implementation guidance for privacy-preserving genomic analysis in multi-institution research consortia.