Abstract:
ML techniques and outsourcing are being increasingly used by researchers in their
efforts to look into and better understand SARS-CoV-2 and combat the spread of
the virus. However, this brings about privacy issues that surround the sharing of,
and training of ML models on, SARS-CoV-2 genomic sequences and contextual data,
potentially leading to the reidentification of the owners of such genomic data. Thus,
there is a need to develop methods of protecting patients’ privacy, all while allowing
researchers and medical professionals to continue the use of ML techniques and outsourcing
to make better informed medical decisions and take more effective actions
against the spread of the virus. To that end, this paper proposes a fully homomorphic
encryption-based viral classification framework and logistic regression model based on
Concrete-ML, a fully open-source FHE ML library.
