AI-Driven Data Anonymization for Privacy-Preserving Healthcare Analytics
Abstract
This paper presents a machine learning-based data anonymization framework to facilitate secure healthcare data sharing while preserving patient privacy. The proposed system utilizes generative adversarial networks (GANs) to create synthetic datasets that maintain statistical integrity without revealing sensitive information. Blockchain is integrated to ensure data traceability and access control. Experimental results highlight its effectiveness in enabling secure and compliant data sharing for research and analytics in healthcare environments.
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