ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets
<|reference_start|>ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets: While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains unique biometric information that poses significant privacy risks. Existing ECG re-identification studies rely on exhaustive analysis of numerous deep learning features, confining to ad-hoc explainability towards clinicians decision making. In this work, we delve into explainability of ECG re-identification risks using transparent machine learning models. We use SHapley Additive exPlanations (SHAP) analysis to identify and explain the key features contributing to re-identification risks. We conduct an empirical analysis of identity re-identification risks using ECG data from five diverse real-world datasets, encompassing 223 participants. By employing transparent machine learning models, we reveal the diversity among different ECG features in contributing towards re-identification of individuals with an accuracy of 0.76 for gender, 0.67 for age group, and 0.82 for participant ID re-identification. Our approach provides valuable insights for clinical experts and guides the development of effective privacy-preserving mechanisms. Further, our findings emphasize the necessity for robust privacy measures in real-world health applications and offer detailed, actionable insights for enhancing data anonymization techniques.<|reference_end|>
@article{wang2024ecg,
    title={ECG Unveiled: Analysis of Client Re-identification Risks in Real-World
  ECG Datasets},
    author={Ziyu Wang, Anil Kanduri, Seyed Amir Hossein Aqajari, Salar Jafarlou,
  Sanaz R. Mousavi, Pasi Liljeberg, Shaista Malik, Amir M. Rahmani},
    journal={arXiv preprint arXiv:2408.10228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10228},
    primaryClass={eess.SP cs.LG}
}