## 1 Introduction  
In the modern era of pervasive digital technology within an increasingly interconnected society, safeguarding privacy and security has emerged as a critical concern. The extensive volume of visual data generated and disseminated daily--via security cameras, social media platforms, and mobile devices--poses substantial privacy risks [1]. Visual media, such as images and videos, frequently encapsulate sensitive information, including personal details, geolocations, and identities, underscoring the necessity for robust privacy protection mechanisms [2]. The evolution of privacy threats has paralleled technological advancement, creating a complex landscape where traditional privacy protection methods often prove insufficient [3]. As technologies become more immersive and pervasive, the lines separating private and public spheres are becoming increasingly blurred due to rising immersion and pervasiveness. This shift highlights the pressing need for novel approaches or advancements in privacy protection [4]. A notable immersive technology in recent years is augmented reality (AR), which superimposes digital data onto the real environment [5]. Through the integration of virtual content with the physical world, AR offers an interactive experience [6]. Its uses are found in several domains, such as industry, gaming, healthcare, and education [7; 8; 9; 10]. However, integrating AR into our daily lives also introduces a new wave of privacy and security concerns [11]. As AR devices continuously scan our surroundings, they inadvertently collect sensitive information, raising concerns about bystander privacy [12]. Furthermore, real-time data processing and storage, coupled with location tracking and behavioral pattern recognition, pose significant risks to the exposure of personal information [13]. The advent of shared AR spaces further complicates the privacy landscape as individuals with diverse privacy preferences engage in a common digital environment [14]. To address these privacy challenges, some techniques can be used to anonymize or obscure visual data captured by AR devices to ensure that the identities and sensitive information of individuals are protected [15]. Common methods include blurring, blocking, and pixelating sensitive information while preserving the overall visual context. However, the effectiveness and suitability of these techniques can vary depending on the specific application, context, and the required level of privacy [13]. Thus, we developed SafeAR ([[https://safear.ipleiria.pt/](https://safear.ipleiria.pt/)]([https://safear.ipleiria.pt/](https://safear.ipleiria.pt/)) accessed on 13 December 2024), a funded project to enhance privacy in AR environments. SafeAR focuses on developing services [16] and libraries [17] to apply privacy-preserving techniques to sensitive visual data captured by AR devices. Using machine learning to identify sensitive data and obfuscation methods such as blurring, blocking, and pixelation, SafeAR seeks to protect the identities of individuals and personal information without compromising the functionality of AR applications. In this paper, we explore user preferences and perceptions of these privacy-enhancing techniques, examining which method is considered more secure or effective in different scenarios. We address the following research questions:  
* **RQ1:** what is the relationship between demographics (age, education, and occupation) and the perceived security of different obfuscation methods? * **RQ2:** how do different obfuscation techniques (masking, pixelation, and blurring) affect user experience in AR applications? * **RQ3:** what is the relationship between the level of familiarity with AR and the acceptance of different obfuscation methods? To answer these questions, we developed and disseminated a questionnaire to a diverse group of participants. The questionnaire was designed to capture information on user demographics, their familiarity with AR, and their perceptions of pixelation, masking, and blurring obfuscation techniques.