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المؤلفAggarwal, Mahima
المؤلفZubair, Mohammed
المؤلفUnal, Devrim
المؤلفAl-Ali, Abdulla
المؤلفReimann, Thomas
المؤلفAlinier, Guillaume
تاريخ الإتاحة2022-03-31T08:51:55Z
تاريخ النشر2022
اسم المنشورJournal of Emergency Medicine, Trauma and Acute Care
المصدرqscience
الاقتباسAggarwal M, Zubair M, Unal D, Al-Ali A, Reimann T, Alinier G. Fuzzy Identification-Based Encryption for healthcare user face authentication, Journal of Emergency Medicine, Trauma & Acute Care 2022:72 http://dx.doi.org/10.5339/jemtac.2022.qhc.72
الرقم المعياري الدولي للكتاب1999-7086
الرقم المعياري الدولي للكتاب1999-7094
معرّف المصادر الموحدhttps://doi.org/10.5339/jemtac.2022.qhc.72
معرّف المصادر الموحدhttp://hdl.handle.net/10576/29203
الملخصBackground: Internet of Medical Things (IOMT) has the potential to monitor health continuously and in real-time. One of the main issues that arise in IOMT is how securely the data can be transmitted to the clinical team. In this project, biometric Identity-based encryption was utilized using the Fuzzy-IBE (Identity-based encryption) scheme that uses face features of the clinicians to create the public key. Figure 1 shows the testbed setup designed to improve the privacy and security of the patients' healthcare data. Methods: The testbed comprises an ESP32 platform sensing and encrypting data, the Nvidia Jetson Nano for data collection and decryption, and the Thingsboard online platform for vital information visualization. Fuzzy Identity-Based Encryption (FIBE) 1-3 uses legitimate users' facial features. The encrypted vital information is transmitted to the Edge-device (Jetson Nano) through BLE/Wi-Fi. On the edge-device of the healthcare system, the face authentication mechanism verifies the user's (clinician) legitimacy to assess the data. Upon user authentication, their facial features will be used to generate a private decryption key that can decrypt the received encrypted data. The data is further sent to the core cloud (Thingsboard) for storage and visualization. To secure the data on the cloud, we deployed an Intrusion Detection System (IDS) model using deep learning to identify the inter-domain stream of malicious traffic. Results: The face authentication testing using Fuzzy Identity-based Cryptography relied on a public data set. The execution time was calculated for Encryption (time to encrypt the patient's vital data using a public key of health physician's facial features) and decryption (time to match at least d components of the ciphertext and perform message decryption). The experimental results are reported in Table 1. Conclusion: In today's age of advanced telecommunication technology, cyber security is a very important factor. The designed testbed setup in this work showcases how healthcare data can be secured against malicious attacks.
اللغةen
الناشرHamad bin Khalifa University Press (HBKU Press)
الموضوعFuzzy Identity based Encryption
Deep Learning
Face biometry
user authentication
Internet of Medical Things
العنوانFuzzy Identification-Based Encryption for healthcare user face authentication
النوعArticle
رقم العدد1
رقم المجلد2022
dc.accessType Open Access


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