The large-scale implementation of Electronic Health Records (EHRs) and IoT-based medical devices in medical healthcare data has transformed current healthcare into an information-driven framework. However, digital health infrastructure mostly relies on cloud-based storage and data- sharing platforms, there exists severe security and privacy concerns as the medical data is sensitive and the cloud systems are more complex. When working with complex medical data from multiple sources, conventional anonymization approaches like suppression, generalization, k-anonymity and l-diversity often fails in securing patient privacy yet retaining the usefulness of the data. To address these problems, new innovations in Artificial Intelligence (AI) have introduced smart anonymization techniques that can automatically balance data privacy and analytical utility. This paper gives a comparative analysis of AI-powered anonymization approaches for securely sharing medical data using cloud systems. It also investigates research gaps, challenges in interoperability, transparency and explainability. To illustrate the strength and weakness of each techniques a comparative analysis framework along with visual analysis are introduced. The results show that federated learning (FL) and diffusion model (DM) provides high privacy protection and transformer- based models demonstrate strong adaptability and data utility. The survey concludes by presenting the future research directions that focuses on explainable AI, adherence to regulations and hybrid methods for anonymity in order to create healthcare networks that are reliable and protect patient privacy.
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AI-Powered Anonymization for Secure Cloud-Based Medical Data Sharing: A Comprehensive Survey
Semantic Scholar · 2025
Abstract
The large-scale implementation of Electronic Health Records (EHRs) and IoT-based medical devices in medical healthcare data has transformed current healthcare into an information-driven framework. However, digital health infrastructure mostly relies on cloud-based storage and data- sharing platforms, there exists severe security and privacy concerns as the medical data is sensitive and the cloud systems are more complex. When working with complex medical data from multiple sources, conventional anonymization approaches like suppression, generalization, k-anonymity and l-diversity often fails in securing patient privacy yet retaining the usefulness of the data. To address these problems, new innovations in Artificial Intelligence (AI) have introduced smart anonymization techniques that can automatically balance data privacy and analytical utility. This paper gives a comparative analysis of AI-powered anonymization approaches for securely sharing medical data using cloud systems. It also investigates research gaps, challenges in interoperability, transparency and explainability. To illustrate the strength and weakness of each techniques a comparative analysis framework along with visual analysis are introduced. The results show that federated learning (FL) and diffusion model (DM) provides high privacy protection and transformer- based models demonstrate strong adaptability and data utility. The survey concludes by presenting the future research directions that focuses on explainable AI, adherence to regulations and hybrid methods for anonymity in order to create healthcare networks that are reliable and protect patient privacy.