Federated Correction of Batch Effects & Heterogeneity in Single-cell and Multi-omics Genomics (privacy-preserving)

Abstract

The rapid proliferation of genomic data from large‑scale sequencing initiatives presents unprecedented opportunities for precision medicine, population genomics and biotechnology. However, the sensitive nature of genomic information—uniquely identifying, immutable and deeply personal—poses critical security and privacy challenges. Traditional methods of data protection (anonymisation, access control) are increasingly inadequate in the face of advanced attacks (membership inference, model inversion) and large‑scale AI analysis. This paper explores the development of artificial intelligence (AI)‑based methods to secure genomic data throughout its lifecycle: from storage and sharing to analysis and model training. We review technical approaches including federated learning, homomorphic encryption, secure multi‑party computation, differential privacy and generative synthetic‑data modelling, each designed to mitigate risk while enabling genomic‑AI workflows. We present a hypothetical benchmarking study where a federated‑learning pipeline augmented with differential‑privacy noise and encrypted aggregation reduced membership inference risk by ~45 % compared with naïve central models, while retaining >90 % of predictive utility. Tabulated results demonstrate trade‑offs between utility, latency and privacy budget. We discuss key methodological details—feature extraction, model architecture, privacy budget calibration—and highlight deployment considerations: interpretability, regulatory compliance (GDPR, HIPAA), adversarial threats and quantum‑resistant cryptography. Future perspectives emphasise hybrid AI‑cryptography frameworks, standardised privacy metrics for genomics, and governance models embedding privacy‑by‑design. In conclusion, AI‑based security methods are critical enablers for responsible genomic‑AI research and clinical translation, offering a path toward privacy‑preserving genomics at scale.