  Motivation: Human genomic datasets often contain sensitive information that
limits use and sharing of the data. In particular, simple anonymisation
strategies fail to provide sufficient level of protection for genomic data,
because the data are inherently identifiable. Differentially private machine
learning can help by guaranteeing that the published results do not leak too
much information about any individual data point. Recent research has reached
promising results on differentially private drug sensitivity prediction using
gene expression data. Differentially private learning with genomic data is
challenging because it is more difficult to guarantee the privacy in high
dimensions. Dimensionality reduction can help, but if the dimension reduction
mapping is learned from the data, then it needs to be differentially private
too, which can carry a significant privacy cost. Furthermore, the selection of
any hyperparameters (such as the target dimensionality) needs to also avoid
leaking private information.
  Results: We study an approach that uses a large public dataset of similar
type to learn a compact representation for differentially private learning. We
compare three representation learning methods: variational autoencoders, PCA
and random projection. We solve two machine learning tasks on gene expression
of cancer cell lines: cancer type classification, and drug sensitivity
prediction. The experiments demonstrate significant benefit from all
representation learning methods with variational autoencoders providing the
most accurate predictions most often. Our results significantly improve over
previous state-of-the-art in accuracy of differentially private drug
sensitivity prediction.
