## 4 Emerging Types of Biometric De-Identification
### Psychological Traits-Based De-Identification  
**Definition 15**. : _Psychological Traits-based De-identification: Psychological traits-based biometric de-identification can be defined as the introduction of perturbation in psychological traits to obfuscate either traditional or auxiliary biometric traits or both of them._  
Personality models have been used extensively by clinical psychologists to study the underlying factors influencing an individual's behavioral patterns [91]. While the users' personality traits have been shown to influence the language used to express themselves and the structure of their social network [92], this concept can be applied to the domain of social behavioral biometric recognition. In the interest of protecting user's privacy on OSN platforms, it is important to study the de-identification of personality traits from social network data. Social network data collected for user identification may also contain information regarding the users' psychological traits. Moreover, the psychological traits information may also be essential for user identification system. In such a scenario, psychological traits-based de-identification refers to the manipulation and storage of social network data in such a way that the personality traits information of users is obfuscated from the stakeholders in a user identification system development process and third parties while preserving the social behavioral user recognition capability from the data. Patterns in social media activity and the contents of social media posts can be analyzed to predict the user's psychological traits. Research has also suggested that personality expressed through OSN platforms can represent unique, permanent, and predictive models of human behavior which can further be used for soft-biometric recognition [93]. Automated systems that classify the psychological traits of individuals via social network data use two prevalent personality scales: the big-five model and the Myers-Briggs Type Indicator (MBTI). The groundwork for applying personality traits-aware social computing systems within the domain of social intelligence and cybersecurity was first established \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Year** & **Biometrics** & \
\begin{tabular}{c} **De-Identified** \\ **Biometrics** \\ \end{tabular} &
\begin{tabular}{c} **Unchanged** \\ **Biometrics** \\ \end{tabular} & **Accuracy of Recognition** \\ \hline [PERSON] et al. [90] & 2006 & Writing style & None & All & 72.43\% on person identification \\ [PERSON] et al. [88] & 2019 & Writing style & None & All & 23.49\% on user verification \\ [PERSON] et al. [89] & 2020 & Linguistic profile & None & All & 99.45\% on person identification \\ [PERSON] et al. [87] & 2020 & Twitter friendship networks & None & All & 94.83\% on user identification \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of social behavioral biometrics-based identification methods. by [PERSON] et al. in 2007 [94]. They demonstrated that the semantic characteristics of an individual's online language can reflect their underlying psychological state. Moreover, recent advances in the field of natural language processing (NLP) have produced powerful language models that can rapidly extract rich feature sets from textual data to be used for further classification tasks [95]. Using such distributed representations of text has shown to be instrumental in deciphering authors psychological traits from a relatively short corpus of their posts on OSN platforms [96]. A classifier trained to predict users' psychological traits based on the discussed models can embed information about the user's personality traits in the low-dimensional representation of data [97]. The count-based metric, such as Term Frequency-Inverse Document Frequency (TF-IDF), was used to extract characteristic features from tweets and was first used in [13].