Large machine learning models with improved predictions have become widely
available in the chemical sciences. Unfortunately, these models do not protect
the privacy necessary within commercial settings, prohibiting the use of
potentially extremely valuable data by others. Encrypting the prediction
process can solve this problem by double-blind model evaluation and prohibits
the extraction of training or query data. However, contemporary ML models based
on fully homomorphic encryption or federated learning are either too expensive
for practical use or have to trade higher speed for weaker security. We have
implemented secure and computationally feasible encrypted machine learning
models using oblivious transfer enabling and secure predictions of molecular
quantum properties across chemical compound space. However, we find that
encrypted predictions using kernel ridge regression models are a million times
more expensive than without encryption. This demonstrates a dire need for a
compact machine learning model architecture, including molecular representation
and kernel matrix size, that minimizes model evaluation costs.

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