SafeMR: Privacy-aware Visual Information Protection for Mobile Mixed Reality

Abstract

Mobile vision technologies have paved the way for augmented (AR) and mixed reality (MR) applications to be realizable on mobile devices. Mobile platforms such as Android and iOS have recently demonstrated the early opportunities for AR/MR applications using their devices. Now, while these technologies can still be considered in its infancy, it is opportune to start thinking about privacy and security while their functionalities are slowly being revealed to us. In this work, we present a visual access control mechanism in the form of objectlevel abstraction. Using readily-available object detection algorithms, we are able to demonstrate a proof-of-concept object-level abstraction for fine-grained access control in a mobile device. Furthermore, aside from the inherent confidentiality and content awareness guarantee of abstraction, reduction in execution times from visual processing resource sharing is another consequential benefit of abstraction without any energy consumption impact.

SoK: Data Privacy in Virtual Reality

“…The collected studies discussing or proposing threat models consider application developers [27,29,30,32,45,59,60,70,72,74,92,126,130,139,161], servers [7,30,92,123], content creators [40,92], device manufacturers [92,131], other users [69,105,125,150], and hackers 1 [40,62,139] as the attackers in VR, or rely on general privacy threat models like Lindunn [31,34,64,70]. Based on these studies and their system decomposition, we adopt a more comprehensive and pervasive privacy-centered attacker classification for VR that encompasses the privacy repercussions of the above threat models.…”

SoK: Data Privacy in Virtual Reality

“…Method. From the 68 selected studies, we identified 5 studies that proposed a VR information flow [37], [84], [25], [137], [46] and 21 with an explicit discussion or proposal for VR defense and (predominantly) threat models [28], [116], [26], [37], [84], [64], [55], [27], [29], [65], [56], [123], [42], [58], [113], [30], [134], [112], [95], [67], [6]. Two researchers extracted, discussed, and combined the associated artifacts, resulting in a holistic VR information flow that frames our threat and defense models.…”

“…The collected studies discussing or proposing threat models consider application developers [26], [84], [64], [55], [27], [29], [65], [56], [123], [42], [113], [30], [67], servers [30], [84], [6], content creators [37], [84], device manufacturers [116], [84], other users [112], [134], [95], and hackers 1 [37], [123], [58] as the attackers in VR, or rely on general privacy threat models like Lindunn [31], [60], [28], [64]. Based on these studies, we adopt a more comprehensive and pervasive privacy-centered attacker classification specific to VR that encompasses the privacy repercussions of the above threat models.…”

Exploring the Privacy Risks of Adversarial VR Game Design

“…A second line of work has focused on identifying specific demographic attributes like age and gender based on VR recordings [5,15,71]. As with the identification studies, these works utilize the passive observation of people using standard VR applications.…”