  As the prevalence of data-driven technologies in healthcare continues to
rise, concerns regarding data privacy and security become increasingly
paramount. This thesis aims to address the vulnerability of personalized
healthcare models, particularly in the context of ECG monitoring, to
adversarial attacks that compromise patient privacy. We propose an approach
termed "Machine Unlearning" to mitigate the impact of exposed data points on
machine learning models, thereby enhancing model robustness against adversarial
attacks while preserving individual privacy. Specifically, we investigate the
efficacy of Machine Unlearning in the context of personalized ECG monitoring,
utilizing a dataset of clinical ECG recordings. Our methodology involves
training a deep neural classifier on ECG data and fine-tuning the model for
individual patients. We demonstrate the susceptibility of fine-tuned models to
adversarial attacks, such as the Fast Gradient Sign Method (FGSM), which can
exploit additional data points in personalized models. To address this
vulnerability, we propose a Machine Unlearning algorithm that selectively
removes sensitive data points from fine-tuned models, effectively enhancing
model resilience against adversarial manipulation. Experimental results
demonstrate the effectiveness of our approach in mitigating the impact of
adversarial attacks while maintaining the pre-trained model accuracy.
