Examining Privacy and Trust Issues at the Edge of Isomorphic IoT Architectures: Case Liquid AI

Examining Privacy and Trust Issues at the Edge of Isomorphic IoT Architectures: Case Liquid AI

Authors: Agbese, M., Mäkitalo, N., Waseem, M., Mohanani, R., Abrahamsson, P., & Mikkonen, T.

Presented at: IoT 2023: Proceedings of the 13th International Conference on the Internet of Things. ACM, pp. 245-252

Significance of the Study

This research delves into the critical aspects of privacy and trust within Isomorphic IoT architectures, utilizing the case study of Liquid AI. With the proliferation of IoT devices and services, ensuring secure and trustworthy interactions at the edge of these architectures is paramount. The study sheds light on the inherent challenges and proposes strategies to enhance privacy and trust in IoT systems, making it a crucial read for professionals and researchers in the field.

Why Read This Paper?

This paper is essential for anyone involved in the development, implementation, or management of IoT systems. It provides valuable insights into the privacy and trust issues that can arise in isomorphic IoT architectures and offers practical solutions to address these challenges. By understanding and applying the findings of this study, stakeholders can significantly improve the security and reliability of their IoT deployments.

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