Privacy-Enhanced Pneumonia Diagnosis: IoT-Enabled Federated Multi-Party Computation in Industry 5.0

Abstract

Pneumonia is a significant global health concern that can lead to severe and sometimes fatal consequences. Timely identification and classification of pneumonia can substantially improve patient outcomes. However, the disclosure of sensitive medical data for diagnostic purposes raises important issues regarding patient privacy and data security. The Federated Multi-Party Computing (FMPC) technique has emerged as a promising solution to these challenges, enabling multiple parties to collaborate and compute a function over their private data while maintaining data privacy. This paper presents a novel Internet of Things (IoT)-enabled FMPC framework that significantly enhances the accuracy of pneumonia categorization while preserving patient privacy and data security. Using publicly available Kaggle datasets, the efficacy of the proposed strategy is assessed, and a comparison with contemporary deep learning models is made. The study demonstrates the remarkable efficacy of the IoT-enabled FMPC approach in pneumonia recognition within the industry 5.0 landscape. With 96.67% accuracy achieved for the unbalanced dataset and 97.84% accuracy for the balanced dataset, the results of the proposed algorithm demonstrate the potential for improvement. This approach ensures the privacy of sensitive medical information, aligning with the core principles of Industry 5.0 that emphasize the harmonious integration of advanced technologies and human-centric values.

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