Authoritative Information and Source Validation in AR

In many use cases and network architectures, data requested by and provided to an AR user for an experience is not pre-loaded on the users device. For a variety of reasons and through a range of technologies, information is streaming from the cloud or edge to the AR device in real time. The reverse is also happening. Sensors on an AR user’s device are capturing the real world and can send during or after world capture salient features or any combination of data (e.g., point clouds, video, audio, etc) to a safe and high capacity storage resource in the cloud or on the edge. Users may also need to use an AR device to annotate, confirm ground truth or add information about the real world to the cloud-based databases. During data creation (annotation) or transfer, there may be opportunities for agents to insert inaccurate or false information, or for corruption.

Depending on use cases or publishers, the level of confidence about accuracy and authorship of information consumed from or added to a remote source will vary. In enterprise or industrial use cases, details about asset authenticity, ownership or conditions for use must be associated with the AR asset in metadata.

This research topic focuses on development of ontologies, protocols and/or standards to ensure and to communicate to or between enterprise systems and users in workplaces that data delivered for use in AR experiences is from an authorized source. In parallel, the research will study or develop on-device authentication systems to validate that data which is added to a pooled (cloud-based) data repository during an AR experience (e.g. tagging information and placing information) is authoritative and the source traceable.

Stakeholders

Network operators, network managers, enterprise IT managers, AR experience publishers, AR experience authoring platforms, AR developers

Possible Methodologies

The research will focus on device encoding and edge computing technologies from multiple suppliers that can support distributed AR architectures. Studies will evaluate use of encryption and authentication technologies on device for suitability in distributed computing. Laboratory-based studies in controlled environments will measure and compare different approaches to data authentication and metadata authoring.

Research Program

This research topic could be part of a program studying the potential benefits of AR Cloud and 5G networks which provide low-latency and high bandwidth connectivity between cloud, edge and AR devices. Other topics such as cloud-based rendering of 3D graphics, discovery of AR experiences and security could be combined with this topic.

Miscellaneous Notes

There is work in the W3C, IETF, IEEE, OMG and other standards bodies on related topics that could contribute to development of best practices and standards for enterprise data management systems.

Keywords

Data capture, data streaming, security, authentication, author, authorship, publication, ownership, remote data repositories, AR cloud, edge computing, spatial computing, access control, data protection, security systems, authentication, message authentication, authorization, data security, access protocols

Research Agenda Categories

Technology

Expected Impact Timeframe

Medium

Related Publications

Using the words in this topic description and Natural Language Processing analysis of publications in the AREA FindAR database, the references below have the highest number of matches with this topic:

• Bermejo, C., Huang, Z., Braud, T., & Hui, P. (2017). When Augmented Reality meets Big Data. 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW).
• Singh, S. (2017). Optimize cloud computations using edge computing. 2017 International Conference on Big Data, IoT and Data Science (BID).
• Ramaseri Chandra, A. N., El Jamiy, F., & Reza, H. (2019). Augmented Reality for Big Data Visualization: A Review. 2019 International Conference on Computational Science and Computational Intelligence (CSCI).
• Lebeck, K., Ruth, K., Kohno, T., & Roesner, F. (2018). Towards Security and Privacy for Multi-user Augmented Reality: Foundations with End Users. 2018 IEEE Symposium on Security and Privacy (SP).
• Khurshid, A., Cleger, S., & Grunitzki, R. (2020). A Scene Classification Approach for Augmented Reality Devices. Lecture Notes in Computer Science, 164-177.

More publications can be explored using the AREA FindAR research tool.

Author

Christine Perey

Last Published (yyyy-mm-dd)

2021-08-31

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