ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914917489049600 |
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| author | Wang, Ziyu Kanduri, Anil Aqajari, Seyed Amir Hossein Jafarlou, Salar Mousavi, Sanaz R. Liljeberg, Pasi Malik, Shaista Rahmani, Amir M. |
| author_facet | Wang, Ziyu Kanduri, Anil Aqajari, Seyed Amir Hossein Jafarlou, Salar Mousavi, Sanaz R. Liljeberg, Pasi Malik, Shaista Rahmani, Amir M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10228 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets Wang, Ziyu Kanduri, Anil Aqajari, Seyed Amir Hossein Jafarlou, Salar Mousavi, Sanaz R. Liljeberg, Pasi Malik, Shaista Rahmani, Amir M. Signal Processing Machine Learning 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. |
| title | ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2408.10228 |