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ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets

International Conference on Body Sensor Networks(2024)

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Abstract
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.
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Biometrics,Electronic healthcare,Health informatics,Machine learning,Privacy preserving,Electrocardiogram
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要点】:论文分析了实际心电图中客户端重识别风险,提出使用透明机器学习模型及SHAP分析方法,揭示了不同ECG特征对个体识别的贡献,为隐私保护提供了新的见解。

方法】:使用SHapley Additive exPlanations (SHAP)分析方法,结合透明机器学习模型,识别并解释导致重识别风险的关键ECG特征。

实验】:对五个不同实际心电数据集(具体名称未提供)的223名参与者进行了身份重识别风险实证分析,实现了性别识别准确率为0.76,年龄组识别准确率为0.67,参与者ID识别准确率为0.82。