PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing
This addresses a critical privacy breach in healthcare data for users of wearable devices, presenting a novel attack method.
The paper tackles the problem of person re-identification as a privacy threat in publicly shared wearable sensing data, achieving 89% to 93% accuracy in re-identifying individuals using physiological and physical contexts.
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.