Sim, J. J., Zhou, W., Chan, F. M., Annamalai, M. S. M. S., Deng, X., Tan, B. H. M., & Aung, K. M. M. (2023). CoVnita, an end-to-end privacy-preserving framework for SARS-CoV-2 classification. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-34535-8
AbstractClassification of viral strains is essential in monitoring and managing the COVID-19 pandemic, but patient privacy and data security concerns often limit the extent of the open sharing of full viral genome sequencing data. We propose a framework called CoVnita, that supports private training of a classification model and secure inference with the same model. Using genomic sequences from eight common SARS-CoV-2 strains, we simulated scenarios where the data was distributed across multiple data providers. Our framework produces a private federated model, over 8 parties, with a classification AUROC of 0.99, given a privacy budget of $$\varepsilon =1$$
. The roundtrip time, from encryption to decryption, took a total of 0.298 s, with an amortized time of 74.5 ms per sample.
Attribution 4.0 International (CC BY 4.0)
This research / project is supported by the Agency for Science, Technology And Research (A*STAR), Singapore - IE2020 Advanced Manufacturing and Engineering (AME) Programmatic Program
Grant Reference no. : A19E3b0099