The PASCAL Visual Object Classes Challenge

Notifying users about a system's data practices is supposed to enable users to make informed privacy decisions. Yet, current notice and choice mechanisms, such as privacy policies, are often ineffective because they are neither usable nor useful, and are therefore ignored by users. Constrained interfaces on mobile devices, wearables, and smart home devices connected in an Internet of Things exacerbate the issue. Much research has studied usability issues of privacy notices and many proposals for more usable privacy notices exist. Yet, there is little guidance for designers and developers on the design aspects that can impact the effectiveness of privacy notices. In this paper, we make multiple contributions to remedy this issue. We survey the existing literature on privacy notices and identify challenges, requirements, and best practices for privacy notice design. Further, we map out the design space for privacy notices by identifying relevant dimensions. This provides a taxonomy and consistent terminology of notice approaches to foster understanding and reasoning about notice options available in the context of specific systems. Our systemization of knowledge and the developed design space can help designers, developers, and researchers identify notice and choice requirements and develop a comprehensive notice concept for their system that addresses the needs of different audiences and considers the system's limitations and opportunities for providing notice.

/pdf/a-design-space-for-effective-privacy-notices-i3j34990y3.pdf

A design space for effective privacy notices

The Augmented Reality (AR) technology enhances the human perception of the world by combining the real environment with the virtual space. With the explosive growth of powerful, less expensive mobile devices, and the emergence of sophisticated communication infrastructure, Mobile Augmented Reality (MAR) applications are gaining increased popularity. MAR allows users to run AR applications on mobile devices with greater mobility and at a lower cost. The emerging 5G communication technologies act as critical enablers for future MAR applications to achieve ultra-low latency and extremely high data rates while Multi-access Edge Computing (MEC) brings enhanced computational power closer to the users to complement MAR. This paper extensively discusses the landscape of MAR through the past and its future prospects with respect to the 5G systems and complementary technology MEC. The paper especially provides an informative analysis of the network formation of current and future MAR systems in terms of cloud, edge, localized, and hybrid architectural options. The paper discusses key application areas for MAR and their future with the advent of 5G technologies. The paper also discusses the requirements and limitations of MAR technical aspects such as communication, mobility management, energy management, service offloading and migration, security, and privacy and analyzes the role of 5G technologies.

/pdf/a-survey-on-mobile-augmented-reality-with-5g-mobile-edge-11f7i8j1c6.pdf

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects

Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.

A Game-Theoretical Approach for User Allocation in Edge Computing Environment

https://uwe-repository.worktribe.com/preview/5974350/1-s2.0-S1474034620300914-main.pdf

A research agenda for augmented and virtual reality in architecture, engineering and construction

The increasing popularity of wearable devices that continuously capture video, and the prevalence of third-party applications that utilize these feeds have resulted in a new threat to privacy. In many situations, sensitive objects/regions are maliciously (or accidentally) captured in a video frame by third-party applications. However, current solutions do not allow users to specify and enforce fine grained access control over video feeds. In this paper, we describe MarkIt, a computer vision based privacy marker framework, that allows users to specify and enforce fine grained access control over video feeds. We present two example privacy marker systems -- PrivateEye and WaveOff. We conclude with a discussion of the computer vision, privacy and systems challenges in building a comprehensive system for fine grained access control over video feeds.

/pdf/markit-privacy-markers-for-protecting-visual-secrets-4zhoaey89w.pdf

MarkIt: privacy markers for protecting visual secrets

Immersive augmented reality (AR) technologies are becoming a reality. Prior works have identified security and privacy risks raised by these technologies, primarily considering individual users or AR devices. However, we make two key observations: (1) users will not always use AR in isolation, but also in ecosystems of other users, and (2) since immersive AR devices have only recently become available, the risks of AR have been largely hypothetical to date. To provide a foundation for understanding and addressing the security and privacy challenges of emerging AR technologies, grounded in the experiences of real users, we conduct a qualitative lab study with an immersive AR headset, the Microsoft HoloLens. We conduct our study in pairs - 22 participants across 11 pairs - wherein participants engage in paired and individual (but physically co-located) HoloLens activities. Through semi-structured interviews, we explore participants' security, privacy, and other concerns, raising key findings. For example, we find that despite the HoloLens's limitations, participants were easily immersed, treating virtual objects as real (e.g., stepping around them for fear of tripping). We also uncover numerous security, privacy, and safety concerns unique to AR (e.g., deceptive virtual objects misleading users about the real world), and a need for access control among users to manage shared physical spaces and virtual content embedded in those spaces. Our findings give us the opportunity to identify broader lessons and key challenges to inform the design of emerging single-and multi-user AR technologies.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNzVoBCS/pdf

Towards Security and Privacy for Multi-user Augmented Reality: Foundations with End Users

This paper presents Kahawai1, a system that provides high-quality gaming on mobile devices, such as tablets and smartphones, by offloading a portion of the GPU computation to server-side infrastructure In contrast with previous thin-client approaches that require a server-side GPU to render the entire content, Kahawai uses collaborative rendering to combine the output of a mobile GPU and a server-side GPU into the displayed output Compared to a thin client, collaborative rendering requires significantly less network bandwidth between the mobile device and the server to achieve the same visual quality and, unlike a thin client, collaborative rendering supports disconnected operation, allowing a user to play offline - albeit with reduced visual quality Kahawai implements two separate techniques for collaborative rendering: (1) a mobile device can render each frame with reduced detail while a server sends a stream of per-frame differences to transform each frame into a high detail version, or (2) a mobile device can render a subset of the frames while a server provides the missing frames Both techniques are compatible with the hardware-accelerated H264 video decoders found on most modern mobile devices We implemented a Kahawai prototype and integrated it with the idTech 4 open-source game engine, an advanced engine used by many commercial games In our evaluation, we show that Kahawai can deliver gameplay at an acceptable frame rate, and achieve high visual quality using as little as one-sixth of the bandwidth of the conventional thin-client approach Furthermore, a 50-person user study with our prototype shows that Kahawai can deliver the same gaming experience as a thin client under excellent network conditions

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/mobi093f-cuervoA.pdf

Demo: Kahawai: high-quality mobile gaming using GPU offload

Augmented reality (AR) technologies, such as Microsoft's HoloLens head-mounted display and AR-enabled car windshields, are rapidly emerging. AR applications provide users with immersive virtual experiences by capturing input from a user's surroundings and overlaying virtual output on the user's perception of the real world. These applications enable users to interact with and perceive virtual content in fundamentally new ways. However, the immersive nature of AR applications raises serious security and privacy concerns. Prior work has focused primarily on input privacy risks stemming from applications with unrestricted access to sensor data. However, the risks associated with malicious or buggy AR output remain largely unexplored. For example, an AR windshield application could intentionally or accidentally obscure oncoming vehicles or safety-critical output of other AR applications. In this work, we address the fundamental challenge of securing AR output in the face of malicious or buggy applications. We design, prototype, and evaluate Arya, an AR platform that controls application output according to policies specified in a constrained yet expressive policy framework. In doing so, we identify and overcome numerous challenges in securing AR output.

Securing Augmented Reality Output

Users are increasingly vulnerable to inadvertently leaking sensitive information through cameras. In this paper, we investigate an approach to mitigating the risk of such inadvertent leaks called privacy markers. Privacy markers give users fine-grained control of what visual information an app can access through a device's camera. We present two examples of this approach: PrivateEye, which allows a user to mark regions of a two-dimensional surface as safe to release to an app, and WaveOff, which does the same for three-dimensional objects. We have integrated both systems with Android's camera subsystem. Experiments with our prototype show that a Nexus 5 smartphone can deliver near realtime frame rates while protecting secret information, and a 26-person user study elicited positive feedback on our prototype's speed and ease-of-use.

https://dl.acm.org/doi/pdf/10.1145/2906388.2906405

Kiron Lebeck

Papers

MarkIt: privacy markers for protecting visual secrets

Towards Security and Privacy for Multi-user Augmented Reality: Foundations with End Users

Demo: Kahawai: high-quality mobile gaming using GPU offload

Securing Augmented Reality Output

What You Mark is What Apps See