Encrypted machine learning of molecular quantum properties. (arXiv:2212.04322v1 [cs.CR])

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 …

encrypted machine machine learning quantum