## 4 Emerging Types of Biometric De-Identification
### Sensor-Based Biometric De-Identification  
**Definition 12**. : _Sensor-based Biometric De-identification: Sensor-based biometric de-identification can be defined as the introduction of perturbation in sensor-based biometric data to obfuscate either traditional or auxiliary biometric traits or both of them._  
Some of the common sensor-based biometrics involves gait sequences and brain signals. Motions of a subject's body joints, while they are walking, represent their gait sequence, and they can be captured using RGB cameras or wearable sensors such as an accelerometer and a gyroscope or a marker-based sensor such as Vicon or a marker-less sensor such as Kinect or a combination thereof [70, 71]. Brain signals are captured using an Electroencephalogram (EEG). EEG measures electrical impulses from several electrodes that are attached to the subject's scalp. The device can directly measure neuronal activity and is the most effective method for measuring neurons [72]. In the gait recognition domain, a biometric de-identification system can be designed by considering the gait as a primary behavioral biometric and the estimations of age, gender, emotion, or activity as auxiliary biometrics [71, 73, 74]. Furthermore, spatial and temporal features extracted over the gait sequence can act as the distinguishing characteristics for the identification of primary and auxiliary biometrics. For brain signal de-identification, a person's identity can remain recognizable while the information about their underlying emotions can be obfuscated. Widespread deployment of sensors in both indoor and outdoor settings resulted in the application development based on biometric characteristics in domains such as kinesiology, physical rehabilitation, smart-home design, and search-and-rescue operations [19, 75, 76]. The appropriate architectural design of the biometric system can enable primary biometric identification and auxiliary biometric estimation. Therefore, perturbations need to be introduced in the data in order to obfuscate either the primary biometric trait or auxiliary biometric traits or both to ensure biometric de-identification. Prior research conceals auxiliary biometric traits while preserving primary biometric traits within the data by introducing a deep learning-based neural style transfer [77]. Obscuring auxiliary biometric traits such as age, gender, activity, and emotion, while retaining the ability to identify a person using their gait can be a topic of future work in sensor-based biometric de-identification. Additionally, perturbing gait sequences to prevent gait-based identification while preserving the auxiliary biometric traits can be another future direction of research. The performance of the de-identification methods of each of the future works can be evaluated by using the established primary and auxiliary biometric identification and estimation methodologies. The methods for identifying the primary biometric or for estimating the auxiliary biometric traits are available in the literature [6]. A deep learning-based approach, such as Generative Adversarial Network (GAN) [78], can be utilized to obtain the optimal perturbation scheme for sensor-based biometric data. In this method, the generator architecture of the network would be responsible for the perturbation and the discriminator architecture would handle the estimation of the primary and auxiliary biometric traits. The architecture of a gait-based behavioral biometric de-identification system is shown in Figure 2. The GAN is trained for the person identification task using either the primary biometric traits or auxiliary biometric traits depending on the desired de-identification mode. The random gait sequences which are perturbed using the generator network are passed into two discriminators, which are distinctly responsible for primary biometric de-identification and auxiliary biometric de-identification.