# A review of homomorphic encryption and software tools for encrypted statistical machine learning

arXiv: https://arxiv.org/abs/1508.06574 · HTML: https://mlsanity.com/paper/1508.06574/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2015.

## Built on by (most cited first, 9 of 9)

- [REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers](https://mlsanity.com/paper/2302.02041/) (2023)
- [Non-IID data and Continual Learning processes in Federated Learning: A long road ahead](https://mlsanity.com/paper/2111.13394/) (2021)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [Application of Homomorphic Encryption in Medical Imaging](https://mlsanity.com/paper/2110.07768/) (2021)
- [Machine Learning in Precision Medicine to Preserve Privacy via Encryption](https://mlsanity.com/paper/2102.03412/) (2021)
- [HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation](https://mlsanity.com/paper/2110.15440/) (2021)
- [Training Natural Language Processing Models on Encrypted Text for Enhanced Privacy](https://mlsanity.com/paper/2305.03497/) (2023)
- [A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example](https://mlsanity.com/paper/2410.22960/) (2024)
- [Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption](https://mlsanity.com/paper/2606.05129/) (2026)

## Abstract (arXiv)

Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework.