A Privacy Protection Model for IoT Healthcare Data Using Federated Learning and Differential Privacy
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Federated Learning (FL) addresses privacy concerns in medical AI by facilitating cooperative model training across several healthcare organizations without exchanging patients' raw data. However, traditional FL still runs the danger of privacy leaking due to gradient exposure. In order to protect data and model parameters, this research aims to provide a privacy-enhanced FL framework that combines Differential Privacy (DP) with Homomorphic Encryption (HE). While HE encrypts communication with encrypted computation, DP adds controlled noise to gradients. Experiments on a real medical dataset for disease classification demonstrate that the suggested FL + DP + HE strategy achieves 90.7% accuracy with good privacy (ε = 1.2) and modest communication cost. For safe healthcare applications, the model provides a good balance between efficiency, privacy, and performance.
Abstract
Abstract Federated Learning (FL) addresses privacy concerns in medical AI by facilitating cooperative model training across several healthcare organizations without exchanging patients' raw data. However, traditional FL still runs the danger of privacy leaking due to gradient exposure. In order to protect data and model parameters, this research aims to provide a privacy-enhanced FL framework that combines Differential Privacy (DP) with Homomorphic Encryption (HE). While HE encrypts communication with encrypted computation, DP adds controlled noise to gradients. Experiments on a real medical dataset for disease classification demonstrate that the suggested FL + DP + HE strategy achieves 90.7% accuracy with good privacy (ε = 1.2) and modest communication cost. For safe healthcare applications, the model provides a good balance between efficiency, privacy, and performance.