Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Privacy preservation in artificial intelligence-enabled healthcare analytics

Li, Shancang, Iqbal, Muddesar, Bashir, Ali Kashif and Xinheng, Wang 2024. Privacy preservation in artificial intelligence-enabled healthcare analytics. Human-Centric Computing and Information Sciences

[thumbnail of HCIS_T2023_Final_pr.pdf]
Preview
PDF - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (986kB) | Preview

Abstract

Emerging techniques such as the Internet of Things (IoT), machine learning, and artificial intelligence (AI) have revolutionized healthcare analytics by offering a multitude of significant benefits, including real-time process, enhanced data efficiency and optimization, enabling offline operation, fostering resilience, personalized and context-aware healthcare, etc. However, privacy concerns are indeed significant when it comes to edge computing and machine learning-enabled healthcare analytics. The training and validation of AI algorithms face considerable obstacles due to privacy concerns and stringent legal and ethical requirements associated with datasets. This work has proposed a healthcare data anonymization framework to address privacy concerns and ensure compliance with data regulations by enhancing privacy protection and anonymizing sensitive information in healthcare analytics, which can maintain a high level of privacy while minimizing any adverse effects on the analytics models. The experimental results have unequivocally showcased the effectiveness of the proposed solution.

Item Type: Article
Status: In Press
Schools: Computer Science & Informatics
Publisher: SpringerOpen / Springer Verlag (Germany)
ISSN: 2192-1962
Date of First Compliant Deposit: 25 June 2024
Date of Acceptance: 25 January 2024
Last Modified: 06 Aug 2024 08:43
URI: https://orca.cardiff.ac.uk/id/eprint/167919

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics