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Braeken, An, Porambage, Pawani, Puvaneswaran, Amirthan, Liyanage, Madhusanka.  2020.  ESSMAR: Edge Supportive Secure Mobile Augmented Reality Architecture for Healthcare. 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech). :1—7.
The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data available in this process, it is also highly vulnerable to all types of security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for both the registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.
Winderickx, Jori, Braeken, An, Singelée, Dave, Peeters, Roel, Vandenryt, Thijs, Thoelen, Ronald, Mentens, Nele.  2018.  Digital Signatures and Signcryption Schemes on Embedded Devices: A Trade-off Between Computation and Storage. Proceedings of the 15th ACM International Conference on Computing Frontiers. :342–347.
This paper targets the efficient implementation of digital signatures and signcryption schemes on typical internet-of-things (IoT) devices, i.e. embedded processors with constrained computation power and storage. Both signcryption schemes (providing digital signatures and encryption simultaneously) and digital signatures rely on computation-intensive public-key cryptography. When the number of signatures or encrypted messages the device needs to generate after deployment is limited, a trade-off can be made between performing the entire computation on the embedded device or moving part of the computation to a precomputation phase. The latter results in the storage of the precomputed values in the memory of the processor. We examine this trade-off on a health sensor platform and we additionally apply storage encryption, resulting in five implementation variants of the considered schemes.