Encrypted cancer genomics and homomorphic encryption benchmarks are addressing the gap between privacy-preserving computation and practical deployment. A new BMC Bioinformatics benchmark evaluates speed versus storage tradeoffs for homomorphic encryption used to analyze genomic data under privacy constraints. The key development for biotech is that privacy methods are being stress-tested for operational feasibility, not just theoretical security—an issue that affects data-sharing consortia and cloud-based analytics in real settings. The result supports decision-making on which encryption approaches can realistically support downstream workflows, including clinical biomarker modeling, where both compute time and system memory are limiting factors.