Differentially Private ECG Heartbeat Classification - Santos, João Volney; Brito, Felipe Timbó; Machado, Javam de Castro (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Automated heartbeat classification models have become crucial for modern cardiac monitoring and the early detection of cardiovascular diseases. However, these models’ tendency to memorize sensitive patient data poses a significant privacy risk. Differential Privacy (DP) has emerged as the mathematical foundation for providing formal, provable privacy guarantees during model training, effectively mitigating risks of data memorization. Despite its robustness, directly applying standard DP algorithms, such as DP-SGD, to neural networks often results in a significant drop in classification performance. We address this challenge by introducing DP-ECG-HC, a privatized architecture that leverages a Conformer-based, tokenized approach to extract morphological patterns from raw ECG signals. Experimental results on real-world ECG datasets demonstrate that DP-ECG-HC preserves robust model utility and effectively even under stringent privacy requirements, outperforming standard private deep learning baselines and achieving a superior privacy-utility trade-off.