Encrypted machine learning of molecular quantum properties

TL;DR

This paper develops secure encrypted machine learning models for predicting molecular quantum properties, addressing privacy concerns in chemical data sharing, but highlights the high computational cost of current encryption methods.

Contribution

The authors implement feasible encrypted ML models using oblivious transfer for molecular property prediction and identify the need for more efficient model architectures.

Findings

Encrypted predictions are a million times more expensive than unencrypted.

Current encryption methods are too costly for practical use in molecular ML.

A compact model architecture is urgently needed to reduce evaluation costs.

Abstract

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.