# Encrypted machine learning of molecular quantum properties

arXiv: https://arxiv.org/abs/2212.04322 · HTML: https://mlsanity.com/paper/2212.04322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 66 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Manipulating SGD with Data Ordering Attacks](https://mlsanity.com/paper/2104.09667/) (2021)
- [Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification](https://mlsanity.com/paper/2202.00580/) (2022)
- [The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos](https://mlsanity.com/paper/1812.05538/) (2018)
- [Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models](https://mlsanity.com/paper/2201.12675/) (2022)
- [CaPC Learning: Confidential and Private Collaborative Learning](https://mlsanity.com/paper/2102.05188/) (2021)
- [FedJAX: Federated learning simulation with JAX](https://mlsanity.com/paper/2108.02117/) (2021)
- [Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting](https://mlsanity.com/paper/2109.07258/) (2021)
- [Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption](https://mlsanity.com/paper/2001.10893/) (2020)

## Abstract (arXiv)

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.