Authentication and Cryptography solutions for Industrial IoT - A Study

Sukumaran, Rajesh P; Benedict, Shajulin

doi:10.1109/i-smac55078.2022.9987278

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…However, such a scheme is susceptible to spoofing or impersonation in complex IoT environments (e.g., smart health, smart home, smart cities). To protect IoT information between any two IoT entities and / or with thirdparty service providers, it is required to use two-way authentication [189] or three-way authentication [190]. d) Authentication methodology: To efficiently manage the identity of the accessing devices, IIoT networks tend to stipulate their architecture in terms of centralization, distribution, or decentralization.…”

Section: B Confidentialitymentioning

confidence: 99%

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

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Section: B Confidentialitymentioning

confidence: 99%