Privacy Preserving Strategies in Artificial Intelligence Enhanced Healthcare Interactions

Abstract

This study presents a novel method for protecting sensitive data in healthcare. To secure private data, we use data collection, anonymization, and sophisticated privacy measures. The strategy emphasizes essential traits and employs reduction and generalization to make records indistinguishable. By carefully adding calculated noise, we can collect data in a way at protects privacy, yielding accurate group results while protecting individual records. The system has various privacy mechanisms. These strategies value data and safeguard privacy. These systems outperform conventional ones in data protection, AI prediction accuracy, and data processing latency. This technique is scalable and secure, giving it a strong foundation for addressing data privacy issues. This strategy enforces healthcare rules and promotes AI app trust. In healthcare, where patient privacy is crucial, it offers a complete and future-proof approach to protecting private data.

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