# Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

arXiv: https://arxiv.org/abs/2204.07752 · HTML: https://mlsanity.com/paper/2204.07752/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems](https://mlsanity.com/paper/2109.14334/) (2021)

## Built on by (most cited first, 11 of 11)

- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning](https://mlsanity.com/paper/2402.06289/) (2024)
- [Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI](https://mlsanity.com/paper/2302.11571/) (2023)
- [A Privacy-Preserving Federated Learning Approach for Kernel methods](https://mlsanity.com/paper/2306.02677/) (2023)
- [Privacy-preserving machine learning for healthcare: open challenges and future perspectives](https://mlsanity.com/paper/2303.15563/) (2023)
- [An advanced data fabric architecture leveraging homomorphic encryption and federated learning](https://mlsanity.com/paper/2402.09795/) (2024)
- [Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks](https://mlsanity.com/paper/2207.00263/) (2022)
- [Private Knowledge Sharing in Distributed Learning: A Survey](https://mlsanity.com/paper/2402.06682/) (2024)
- [Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals](https://mlsanity.com/paper/2209.13007/) (2022)
- [Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices](https://mlsanity.com/paper/2308.14658/) (2023)
- [Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks](https://mlsanity.com/paper/2412.01650/) (2024)

## Abstract (arXiv)

Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applications using medical data. One of the solutions for this problem is homomorphic encryption-based model protection from the adversary collaborator. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption. The proposed algorithm uses a secure multi-party computation protocol to protect the deep learning model from the adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance.