# Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/2505.12869 · HTML: https://mlsanity.com/paper/2505.12869/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 5 of 5)

- [Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption](https://mlsanity.com/paper/2104.06824/) (2021)
- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Better Private Linear Regression Through Better Private Feature Selection](https://mlsanity.com/paper/2306.00920/) (2023)
- [Secure Two-Party Feature Selection](https://mlsanity.com/paper/1901.00832/) (2019)
- [Privacy-Preserving Feature Selection with Fully Homomorphic Encryption](https://mlsanity.com/paper/2110.05088/) (2021)

## Abstract (arXiv)

Feature selection is a technique that extracts a meaningful subset from a set of features in training data. When the training data is large-scale, appropriate feature selection enables the removal of redundant features, which can improve generalization performance, accelerate the training process, and enhance the interpretability of the model. This study proposes a privacy-preserving computation model for feature selection. Generally, when the data owner and analyst are the same, there is no need to conceal the private information. However, when they are different parties or when multiple owners exist, an appropriate privacy-preserving framework is required. Although various private feature selection algorithms, they all require two or more computing parties and do not guarantee security in environments where no external party can be fully trusted. To address this issue, we propose the first outsourcing algorithm for feature selection using fully homomorphic encryption. Compared to a prior two-party algorithm, our result improves the time and space complexity O(kn^2) to O(kn log^3 n) and O(kn), where k and n denote the number of features and data samples, respectively. We also implemented the proposed algorithm and conducted comparative experiments with the naive one. The experimental result shows the efficiency of our method even with small datasets.