PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing
| Autor: | Sullivan, James O, Alam, Mohammad Arif Ul |
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| Rok vydání: | 2022 |
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| Zdroj: | The 18th International Conference on Mobility, Sensing and Networking (MSN 2022) |
| Druh dokumentu: | Working Paper |
| Popis: | Person re-identification is a critical privacy breach in publicly shared healthcare data. We investigate the possibility of a new type of privacy threat on publicly shared privacy insensitive large scale wearable sensing data. In this paper, we investigate user specific biometric signatures in terms of two contextual biometric traits, physiological (photoplethysmography and electrodermal activity) and physical (accelerometer) contexts. In this regard, we propose PhysioGait, a context-aware physiological signal model that consists of a Multi-Modal Siamese Convolutional Neural Network (mmSNN) which learns the spatial and temporal information individually and performs sensor fusion in a Siamese cost with the objective of predicting a person's identity. We evaluated PhysioGait attack model using 4 real-time collected datasets (3-data under IRB #HP-00064387 and one publicly available data) and two combined datasets achieving 89% - 93% accuracy of re-identifying persons. Comment: Accepted in IEEE MSN 2022. arXiv admin note: substantial text overlap with arXiv:2106.11900 |
| Databáze: | arXiv |
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