论文标题
物理学：对可穿戴感应的人重新识别攻击的情境感知的生理上下文建模
PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing
论文作者
论文摘要
人重新识别是公开共享医疗保健数据中的严重隐私漏洞。我们调查了公开共享隐私的新型隐私威胁的可能性不敏感的大规模可穿戴感应数据。在本文中，我们根据两个上下文生物识别特征，生理学（光摄影学和电肌活动）和物理（加速度计）上下文来研究用户特定的生物特征特征。在这方面，我们提出了Physiogait，这是一种情境感知的生理信号模型，该模型由多模式的暹罗卷积神经网络（MMSNN）组成，该模型单独学习空间和时间信息，并以暹罗成本执行传感器融合，以预测一个人的身份。我们使用4个实时收集的数据集（IRB＃＃HP-00064387和一个公开可用数据）和两个合并的数据集评估了物理学攻击模型，可重新识别的人的精度达到89％-93％。
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.
