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With this generation of devices, Virtual Reality (VR) has actually made it into the living rooms of end-users. These devices feature 6-DOF tracking, allowing them to move naturally in virtual worlds and experience them even more immersively. However, for a natural locomotion in the virtual, one needs a corresponding free space in the real environment. The available space is often limited, especially in everyday environments and under normal spatial conditions. Furnishings and objects of daily life can quickly become obstacles for VR users if they are not cleared away. Since the idea behind VR is to place users into a virtual world and to hide the real world as much as possible, invisible objects represent potential obstacles. The currently available systems offer only rudimentary assistance for this problem. If a user threatens to leave the space previously defined for use, a visual boundary is displayed to allow orientation within the space. These visual metaphors are intended to prevent users from leaving the safe area. However, there is no detection of potentially dangerous objects within this part of space. Objects that have not been cleared away or that have been added in the meantime may still become obstacles. This thesis shows how possible obstacles in the environment can be detected automatically with range imaging cameras and how users can be effectively warned about them in the virtual environment without significantly disturbing their sense of presence. Four different interactive visual metaphors are used to signalize the obstacles within the VE. With the help of a user study, the four signaling variants and the obstacle detection were evaluated and tested.
In the work at hand, we state that privacy and malleability of data are two aspects highly desired but not easy to associate. On the one hand, we are trying to shape data to make them usable and editable in an intelligible way, namely without losing their initial information. On the other hand, we are looking for effective privacy on data such that no external or non-authorized party could learn about their content. In such a way, we get overlapping requirements by pursuing different goals; it is trivial to be malleable without being secure, and vice versa. We propose four “real-world” use cases identified as scenarios where these two contradictory features are required and taking place in distinct environments. These considered backgrounds consist of firstly, cloud security auditing, then privacy of mobile network users and industry 4.0 and finally, privacy of COVID-19 tracing app users. After presenting useful background material, we propose to employ multiple approaches to design solutions to solve the use cases. We combine homomorphic encryption with searchable encryption and private information retrieval protocol to build an effective construction for the could auditing use case. As a second step, we develop an algorithm to generate the appropriate parameters to use the somewhat homomorphic encryption scheme by considering correctness, performance and security of the respective application. Finally, we propose an alternative use of Bloom filter data structure by adding an HMAC function to allow an outsourced third party to perform set relations in a private manner. By analyzing the overlapping bits occurring on Bloom filters while testing the inclusiveness or disjointness of the sets, we show how these functions maintain privacy and allow operations directly computed on the data structure. Then, we show how these constructions could be applied to the four selected use cases. Our obtained solutions have been implemented and we provide promising results that validate their efficiency and thus relevancy.
Das Ziel der Arbeit ist es, die Wirkung von datenschutzbezogenen Gütesiegeln auf das Vertrauen, die Teilnahmebereitschaft und die freiwillige Datenbekanntgabe in Webbefragungen zu untersuchen. Hierbei soll der unternehmerische Nutzen im Kontext der deutschen Markt-, Medien- und Sozialforschung transparent gemacht werden. Da sich an diesem Markt überwiegend kleine und mittlere Forschungseinrichtungen befinden, werden die wirtschaftlichen Belange dieser Unternehmen besonders berücksichtigt. Insgesamt beschäftigt sich die Arbeit durch den besonderen Branchenbezug zur deutschen Markt-, Medien- und Sozialforschung mit einem neuartigen Forschungsfeld. Vor diesem Hintergrund werden die konzeptionellen und theoretischen Grundlagen, die zum Einsatz von Gütesiegeln im E-Commerce vorliegen, in einem neuen Licht betrachtet. Dabei liegt die Besonderheit der Arbeit darin, dass sie sich mit der freiwilligen Bekanntgabe von persönlichen Daten auf der Basis von intrinsisch motivierten Faktoren befasst.