# ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies

arXiv: https://arxiv.org/abs/2110.11446 · HTML: https://mlsanity.com/paper/2110.11446/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Secure Collaborative Training and Inference for XGBoost](https://mlsanity.com/paper/2010.02524/) (2020)
- [Privacy-Preserving XGBoost Inference](https://mlsanity.com/paper/2011.04789/) (2020)

## Built on by (most cited first, 1 of 1)

- [Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs](https://mlsanity.com/paper/2503.09184/) (2025)

## Abstract (arXiv)

Preserving the privacy and security of big data in the context of cloud computing, while maintaining a certain level of efficiency of its processing remains to be a subject, open for improvement. One of the most popular applications epitomizing said concerns is found to be useful in genome analysis. This work proposes a secure multi-label tumor classification method using homomorphic encryption, whereby two different machine learning algorithms, SVM and XGBoost, are used to classify the encrypted genome data of different tumor types.