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Register a new userSecurity and Privacy Approaches in Mixed Reality: A Literature Survey
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Added by
Jaybie de Guzman
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Mixed reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. With most of the research and development focused on delivering the promise of MR, there is only barely a few working on the privacy and security implications of this technology. This survey paper aims to put in to light these risks, and to look into the latest security and privacy work on MR. Specifically, we list and review the different protection approaches that have been proposed to ensure user and data security and privacy in MR. We extend the scope to include work on related technologies such as augmented reality (AR), virtual reality (VR), and human-computer interaction (HCI) as crucial components, if not the origins, of MR, as well as numerous related work from the larger area of mobile devices, wearables, and Internet-of-Things (IoT). We highlight the lack of investigation, implementation, and evaluation of data protection approaches in MR. Further challenges and directions on MR security and privacy are also discussed.
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In this study, we examine the ways in which user attitudes towards privacy and security relating to mobile devices and the data stored thereon may impact the strength of unlock authentication, focusing on Androids graphical unlock patterns. We conducted an online study with Amazon Mechanical Turk ($N=750$) using self-reported unlock authentication choices, as well as Likert scale agreement/disagreement responses to a set of seven privacy/security prompts. We then analyzed the responses in multiple dimensions, including a straight average of the Likert responses as well as using Principle Component Analysis to expose latent factors. We found that responses to two of the seven questions proved relevant and significant. These two questions considered attitudes towards general concern for data stored on mobile devices, and attitudes towards concerns for unauthorized access by known actors. Unfortunately, larger conclusions cannot be drawn on the efficacy of the broader set of questions for exposing connections between unlock authentication strength (Pearson Rank $r=-0.08$, $p<0.1$). However, both of our factor solutions exposed differences in responses for demographics groups, including age, gender, and residence type. The findings of this study suggests that there is likely a link between perceptions of privacy/security on mobile devices and the perceived threats therein, but more research is needed, particularly on developing better survey and measurement techniques of privacy/security attitudes that relate to mobile devices specifically.
Wearable devices generate different types of physiological data about the individuals. These data can provide valuable insights for medical researchers and clinicians that cannot be availed through traditional measures. Researchers have historically relied on survey responses or observed behavior. Interestingly, physiological data can provide a richer amount of user cognition than that obtained from any other sources, including the user himself. Therefore, the inexpensive consumer-grade wearable devices have become a point of interest for the health researchers. In addition, they are also used in continuous remote health monitoring and sometimes by the insurance companies. However, the biggest concern for such kind of use cases is the privacy of the individuals. There are a few privacy mechanisms, such as abstraction and k-anonymity, are widely used in information systems. Recently, Differential Privacy (DP) has emerged as a proficient technique to publish privacy sensitive data, including data from wearable devices. In this paper, we have conducted a Systematic Literature Review (SLR) to identify, select and critically appraise researches in DP as well as to understand different techniques and exiting use of DP in wearable data publishing. Based on our study we have identified the limitations of proposed solutions and provided future directions.
Augmented reality (AR) or mixed reality (MR) platforms require spatial understanding to detect objects or surfaces, often including their structural (i.e. spatial geometry) and photometric (e.g. color, and texture) attributes, to allow applications to place virtual or synthetic objects seemingly anchored on to real world objects; in some cases, even allowing interactions between the physical and virtual objects. These functionalities require AR/MR platforms to capture the 3D spatial information with high resolution and frequency; however, these pose unprecedented risks to user privacy. Aside from objects being detected, spatial information also reveals the location of the user with high specificity, e.g. in which part of the house the user is. In this work, we propose to leverage spatial generalizations coupled with conservative releasing to provide spatial privacy while maintaining data utility. We designed an adversary that builds up on existing place and shape recognition methods over 3D data as attackers to which the proposed spatial privacy approach can be evaluated against. Then, we simulate user movement within spaces which reveals more of their space as they move around utilizing 3D point clouds collected from Microsoft HoloLens. Results show that revealing no more than 11 generalized planes--accumulated from successively revealed spaces with large enough radius, i.e. $rleq1.0m$--can make an adversary fail in identifying the spatial location of the user for at least half of the time. Furthermore, if the accumulated spaces are of smaller radius, i.e. each successively revealed space is $rleq 0.5m$, we can release up to 29 generalized planes while enjoying both better data utility and privacy.
Gamification and Serious Games are progressively being used over a host of fields, particularly to support education. Such games provide a new way to engage students with content and can complement more traditional approaches to learning. This article proposes SherLOCKED, a new serious game created in the style of a 2D top-down puzzle adventure. The game is situated in the context of an undergraduate cyber security course, and is used to consolidate students knowledge of foundational security concepts (e.g. the CIA triad, security threats and attacks and risk management). SherLOCKED was built based on a review of existing serious games and a study of common gamification principles. It was subsequently implemented within an undergraduate course, and evaluated with 112 students. We found the game to be an effective, attractive and fun solution for allowing further engagement with content that students were introduced to during lectures. This research lends additional evidence to the use of serious games in supporting learning about cyber security.
Additive manufacturing (AM) is growing as fast as anyone can imagine, and it is now a multi-billion-dollar industry. AM becomes popular in a variety of sectors, such as automotive, aerospace, biomedical, and pharmaceutical, for producing parts/ components/ subsystems. However, current AM technologies can face vast risks of security issues and privacy loss. For the security of AM process, many researchers are working on the defense mechanism to countermeasure such security concerns and finding efficient ways to eliminate those risks. Researchers have also been conducting experiments to establish a secure framework for the users privacy and security components. This survey consists of four sections. In the first section, we will explore the relevant limitations of additive manufacturing in terms of printing capability, security, and possible solutions. The second section will present different kinds of attacks on AM and their effects. The next part will analyze and discuss the mechanisms and frameworks for access control and authentication for AM devices. The final section examines the security issues in various industrial sectors and provides the observations on the security of the additive manufacturing process.
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