Enhanced Consumer Healthcare Data Protection Through AI-Driven TinyML and Privacy-Preserving Techniques

In the contemporary digital landscape, securing healthcare data stored on mobile devices has become imperative due to the increasing prevalence of cyberattacks. Healthcare data, often vulnerable to breaches, requires robust privacy-preserving mechanisms to ensure secure storage and sharing across various platforms. This study proposes a novel approach integrating TinyML (machine learning) with federated learning (FL) and differential privacy (DP) to enhance the security and privacy of healthcare data on resource-constrained mobile devices. The proposed TinyML model processes patient data, including ECG readings and cardiac arrhythmia diagnoses, directly on handheld devices, enabling real-time analysis with minimal resource consumption. Federated learning facilitates decentralized training of models on local devices, ensuring that sensitive data remains on user devices while model parameters are aggregated centrally. Differential privacy further strengthens security by introducing controlled noise into the data, safeguarding against malicious attacks without compromising data utility. The system demonstrates a high accuracy of 99%, significantly outperforming traditional models such as Decision Tree (76.0%), Random Forest (98.1%), and Federated Learning with Deep Q (95%). The proposed framework offers a scalable and efficient solution for real-time healthcare data processing, ensuring data privacy and security across distributed environments. Experimental results validate the model’s effectiveness in safeguarding healthcare data, with implications for broader applications in secure, privacy-preserving medical data analysis.