## 3 Methodology
** The third section focused on privacy and obfuscation awareness, introducing participants to fundamental concepts of privacy and obfuscation in digital contexts. As shown in the Appendix in Table A3, we examined the participants' perceptions of different obfuscation techniques (masking, pixelating, and blurring) across a variety of AR contexts, as can be seen in Figure 2. This section aimed to evaluate the suitability of each technique for different scenarios, such as outdoor environments (bike or people) and indoor environments (document or screen). They were asked to rate how appropriate each obfuscation technique is for these contexts on a scale ranging from \"Not at all\" to \"Extremely\", offering insights into their preferences for privacy protection based on situational needs. In addition to contextual suitability, the questionnaire also explored participants' perceptions of using each obfuscation method across all AR contexts. Specifically, it  
Figure 2: Examples of obfuscated images in different AR contexts (indoor and outdoor), demonstrating techniques such as masking, pixelation, and blurring. addressed aesthetic appeal, information preservation, satisfaction, security, and trust, allowing us to assess how these factors influence users' approaches toward each technique. **The adoption and application of obfuscation techniques. ** The final section, detailed in the Appendix in Table A4, offered an overview of users' experiences and approaches toward incorporating obfuscation techniques into their daily lives. Participants were asked to evaluate the suitability of obfuscation techniques, such as masking, pixelating, and blurring, in different AR contexts. In addition to this, the interest in having control over the degree of obfuscation applied provided insights into how much autonomy users expect when managing privacy settings. It also assessed their familiarity with obfuscation applications by asking if they had ever used apps to obfuscate data. Over the course of a week, the full data collection process was conducted. An introductory note outlining the goal of this study and its relationship to the project was included at the start of the questionnaire. In order to balance collecting thorough data with preserving participant involvement, the survey was developed so that it could take less than 10 min to complete. No personal identifiable information was taken during the procedure, and all replies were gathered anonymously to protect participant privacy. After collecting the data from the questionnaire, the results were analyzed using the Pandas library [33], which facilitated efficient data manipulation and analysis. Necessary libraries for data analysis and visualization include Pandas, NumPy, Seaborn, Matplotlib, SciPy, and Scikit-learn [34]. The analysis focused on understanding the relationship between the level of familiarity with AR and the acceptance of different obfuscation methods (masking, pixelation, and blurring). Various statistical methods and visualizations were employed to analyze the data. Descriptive statistics were calculated, including the mean, median, standard deviation, minimum, and maximum values for satisfaction ratings across different AR familiarity levels. Boxplots were used to visualize the distribution of satisfaction ratings for each obfuscation method across different AR familiarity levels. The Kruskal-Wallis (H) test [35] was conducted to determine if there were statistically significant differences between the satisfaction ratings of different obfuscation methods.