PriMR — Design and evaluation of scalable user interfaces for communication and control of data protection aspects in mixed reality

Principal Investigators

Prof. Dr. Florian Alt, Bundeswehr University Munich, Munich (Homepage)
Prof. Dr. Stefan Schneegass, University of Duisburg-Essen, Essen (Homepage)

Main Research Questions

How can MR user interfaces increase awareness of what data is collected, processed and passed on?

Sub-Research Questions
  1. How can users’ privacy behaviour be studied in realistic environments?
  2. How can passive users of MR users be informed about ongoing tracking and how can they be granted control over their data?
  3. How can user interfaces communicate the privacy risks associated with consent to data collection and sharing efficiently and at appropriate moments?
  4. How can MR user interfaces support efficient privacy consent?
  5. How can the impact of MR privacy interfaces be assessed?
  6. How can researchers and practitioners be supported in designing MR applications in a data protection-compliant manner?

Mixed Reality (MR) headsets enable numerous new applications, including in the areas of leisure, work, education and marketing. With MR, users can immerse themselves in a virtual world or expand their view of the real world with virtual content. To achieve this, MR headsets use a series of sensors that can collect, process and share sensitive data with third parties. Modern headsets enable access to behavioral data (hand and body movements, gaze), physiological data (EEG, heart rate), and contextual data (tracking room, passive users). Information about demographics, health status and disabilities can be derived from such data. It is obvious that such data is sensitive. While sensors are required to enable tracking and interaction, the data collected can be misused. This presents a challenge as access to the data is necessary to create an immersive user experience. At the same time, it is important to enable active and passive users to protect their data from unintentional use. This project investigates how to develop privacy control user interfaces for MR. The core challenges are (1) how to sensitize active and passive users to the privacy implications of using MR technology and (2) how to support them in making meaningful decisions regarding data collection, processing and sharing. As MR is used in a variety of environments, supports a growing number of applications (gaming, office, education), continually integrates novel sensors, and engages users with different abilities. The project is an important step towards making data protection an integral aspect in the development of MR applications.