PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing
- Resource Type
- Conference
- Authors
- Sullivan, James; Ul Alam, Mohammad Arif
- Source
- 2022 18th International Conference on Mobility, Sensing and Networking (MSN) MSN Mobility, Sensing and Networking (MSN), 2022 18th International Conference on. :331-338 Dec, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Heart rate
Data privacy
Biometrics (access control)
Wearable computers
Medical services
Data models
Sensors
Siamese Network
Neural Network
Activity Recognition
Security and Privacy
Wearable sensing
Physiological Sensing
- Language
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 phyiological 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 collected datasets and one publicly available data) and two combined datasets achieving 89% - 93% accuracy of re-identifying persons.