Advanced Techniques for Protecting Privacy in Artificial Intelligence Powered Medical Systems

Abstract

Privacy protection is required when analyzing healthcare data and using it effectively. This work proposes a novel technique to secure private data in AI-powered medical systems while providing important data insights. The recommended method incorporates challenging techniques such as integrating Laplace distribution noise, managing secure data, and training group models. A privacy budget manages settings to balance analysis performance and individual contributions. The framework outperforms existing approaches in accuracy, precision, memory, F1 score, and privacy compliance. The technique improves data, models, training, and inference speeds, making it suitable for real-time healthcare applications. Iterative feedback enhances the model by modifying components based on real-world data. In addition to ensuring privacy, this entire design enables AI-powered medical systems to identify and anticipate findings. It provides a true, scalable solution that can adapt to healthcare demands, creating a new standard for AI app privacy and data usage. The technology provides a privacy-protected, highly efficient model that enhances decision-making and patient outcomes, enabling AI in healthcare.

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