## 3 Comprehensive Classification of Existing De-identification Methods  
Privacy of biometric data is of paramount importance [20]. The research on biometric de-identification originated about a decade ago, with earlier works considering complete de-identification as the primary method of ensuring user privacy. This section summarizes key findings in the domains of traditional and soft biometric de-identification and classifies existing research studies into the proposed de-identification categories. 1. _Complete de-identification_ refers to the modification of the original biometric trait such that the identifying information is lost. A comprehensive review of methods for visual data concealment is found in the work by [PERSON] et al. [21]. They include filtering, encryption, reduction through k-same methods or object removal, visual abstraction, and data hiding. [PERSON] et al. [22] used blurring techniques for complete face de-identification. Their process applied a blurring filter on the localized portion of a face. They also applied the pixelization and masking method to measure its impact on face de-identification. Subsequently, they performed a masking operation on a face image to fully prevent a biometric recognition system from identifying a face. Another work by [PERSON] et al. [23] proposed a reversible complete de-identification method based on reallocating pixels in an original biometric. Recently, a complete generative de-identification system for full body and face was developed, utilizing an adaptive approach to de-identification [24]. In 2020, an interesting study [25] considered an effect of video data reduction on user awareness of privacy. [PERSON] et al. [26] used hypersphere projection and singular value decomposition (SVD) to perform de-identification. Behavioral biometric de-identification is also a popular topic. In one of the earlier works on speaker anonymization, [PERSON] et al. [27] proposed a speaker de-identification system to prevent revealing the identity of the speaker to unauthorized listeners. The authors used a Gaussian Mixture Model (GMM) and a Phonetic approach for voice transformation and compared the performance. [PERSON] et al. [28] suggested a speaker de-identification and re-identification model to secure the identity of the speaker from unauthorized listeners. In [29], the authors developed a speaker anonymization system by synthesizing the linguistic and speaker identity features from speech using neural acoustic and waveform models. [PERSON] et al. [30] designed an irreversible speaker anonymization system using the McAdams coefficient to convert the spectral envelope of voice signals. Most recently, [PERSON] et al. [31] presented a voice anonymization system that improved the anonymity of the existing x-vector by learning the distributional properties of the vector space. The generated anonymous voices were highly dissimilar and diverse from the original speakers while preserving the intra-similarity distribution. One of the most recent works on gait de-identification was carried out in 2019 by [PERSON] et al. [32]. They developed a gait de-identification system based on Spatio-Temporal Generative Adversarial Network (ST-GAN). The network incorporated noise in the gait distribution to synthesize the gait sequences for anonymization. In [33], the authors proposed a method that produced fully anonymized speech by adopting many-to-many voice transformation techniques based on variational autoencoders (VAEs). The method changed speaker's identity vectors of the VAE input in order to anonymize the speech data. The summary of complete de-identification research is presented in Table 1.  
_Soft biometrics preserving de-identification_ aims to remove traditional biometric traits from the data while retaining the soft or auxiliary traits. For example, in gait recognition, clothing information can be retained, while gait patterns are rendered unrecognizable. The majority of research in this category has been performed on face biometric.