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The accelerating growth of omics technologies, combined with the increasing availability of structured medical records, offers extraordinary opportunities to advance our understanding of human health and disease. At the same time, these developments pose significant challenges for safeguarding patient privacy and integrating sensitive data across institutions. In this talk, I will present our lab’s work on privacy-preserving informatics and machine learning methods that support critical biomedical analyses without requiring raw data to be centralized or shared. In particular, I will highlight how approaches such as homomorphic encryption, federated learning, and secure multiparty computation can enable knowledge discovery while maintaining rigorous privacy protections.