# High Performance Logistic Regression for Privacy-Preserving Genome Analysis

arXiv: https://arxiv.org/abs/2002.05377 · HTML: https://mlsanity.com/paper/2002.05377/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

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

- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant](https://mlsanity.com/paper/2201.10838/) (2022)
- [Secure Multiparty Computation for Synthetic Data Generation from Distributed Data](https://mlsanity.com/paper/2210.07332/) (2022)
- [Privacy-Preserving Training of Tree Ensembles over Continuous Data](https://mlsanity.com/paper/2106.02769/) (2021)
- [PrivFair: a Library for Privacy-Preserving Fairness Auditing](https://mlsanity.com/paper/2202.04058/) (2022)
- [Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation](https://mlsanity.com/paper/2101.07365/) (2021)
- [Training Differentially Private Models with Secure Multiparty Computation](https://mlsanity.com/paper/2202.02625/) (2022)
- [Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings](https://mlsanity.com/paper/2609.03376/) (2026)
- [Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines](https://mlsanity.com/paper/2103.11240/) (2021)

## Abstract (arXiv)

In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure Multi-Party Computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.