High security and privacy protection model for STI/HIV risk prediction.

ABSTRACT:

Introduction

Applying and leveraging artificial intelligence within the healthcare domain has emerged as a fundamental pursuit to advance health. Data-driven models rooted in deep learning have become powerful tools for use in healthcare informatics. Nevertheless, healthcare data are highly sensitive and must be safeguarded, particularly information related to sexually transmissible infections (STIs) and human immunodeficiency virus (HIV).

Methods

We employed federated learning (FL) in combination with homomorphic encryption (HE) for STI/HIV prediction to train deep learning models on decentralized data while upholding rigorous privacy. The dataset included 168,459 data entries collected from eight countries between 2013 and 2018. The data for each country was split into two

SUBMITTER: Tang Z 

PROVIDER: S-EPMC11580078 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature