Seamless and Secure VR: Adapting and Evaluating Established Authentication Systems for Virtual Reality

Virtual reality (VR) headsets are enabling a wide range of new opportunities for the user. For example, in the near future users may be able to visit virtual shopping malls and virtually join international conferences. These and many other scenarios pose new questions with regards to privacy and security, in particular authentication of users within the virtual environment. As a first step towards seamless VR authentication, this paper investigates the direct transfer of well-established concepts (PIN, Android unlock patterns) into VR. In a pilot study (N = 5) and a lab study (N = 25), we adapted existing mechanisms and evaluated their usability and security for VR. The results indicate that both PINs and patterns are well suited for authentication in VR. We found that the usability of both methods matched the performance known from the physical world. In addition, the private visual channel makes authentication harder to observe, indicating that authentication in VR using traditional concepts already achieves a good balance in the trade-off between usability and security. The paper contributes to a better understanding of authentication within VR environments, by providing the first investigation of established authentication methods within VR, and presents the base layer for the design of future authentication schemes, which are used in VR environments only

Exploratory Study of the Privacy Extension for System Theoretic Process Analysis (STPA-Priv) to elicit Privacy Risks in eHealth

Context: System Theoretic Process Analysis for Privacy (STPA-Priv) is a novel privacy risk elicitation method using a top down approach. It has not gotten very much attention but may offer a convenient structured approach and generation of additional artifacts compared to other methods. Aim: The aim of this exploratory study is to find out what benefits the privacy risk elicitation method STPA-Priv has and to explain how the method can be used. Method: Therefore we apply STPA-Priv to a real world health scenario that involves a smart glucose measurement device used by children. Different kinds of data from the smart device including location data should be shared with the parents, physicians, and urban planners. This makes it a sociotechnical system that offers adequate and complex privacy risks to be found. Results: We find out that STPA-Priv is a structured method for privacy analysis and finds complex privacy risks. The method is supported by a tool called XSTAMPP which makes the analysis and its results more profound. Additionally, we learn that an iterative application of the steps might be necessary to find more privacy risks when more information about the system is available later. Conclusions: STPA-Priv helps to identify complex privacy risks that are derived from sociotechnical interactions in a system. It also outputs privacy constraints that are to be enforced by the system to ensure privacy.Comment: author's post-prin

ArcAid interactive archery assistant

This paper describes the design process of a bow aiming system, called ArcAid, which is an interactive archery assistant. The main goal of ArcAid is to introduce a way for beginner Robin Hoods to learn the art of archery to its fullest. In order to achieve this goal, our smartphone-based design focuses on a fun and interactive learning process that gives constant feedback to the user on how to hit a certain goal. A SPIKE high- end laser sensor is used for the distance measurement and the smartphone’s accelerometer is used to define the angle of inclination. To measure the force on the arrow and the displacement of the string, a flex sensor is attached upon one of the arcs of the bow. All sensor data is processed in an Arduino Nano microprocessor and feedback to the user is given by a dedicated smartphone app. In this paper, we mainly focus on the construction, mechanics and electronics of the ArcAid bow and on the design of the mobile app, which is the game controller. Furthermore, we briefly discuss some future development ideas

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomously. Falling in between the two extremes of mediated perception and behavior reflex, we argue that our direct perception representation provides the right level of abstraction. To demonstrate this, we train a deep Convolutional Neural Network using recording from 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments. We also train a model for car distance estimation on the KITTI dataset. Results show that our direct perception approach can generalize well to real driving images. Source code and data are available on our project website