Privacy Preservation in Artificial Intelligence-Enabled Healthcare Analytics

Emerging techniques such as the Internet of Things, 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.