  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.

Signal Processing;Machine Learning