Fuzzy Identification-Based Encryption for healthcare user face authentication

Aggarwal, Mahima, Zubair, Mohammed, Unal, Devrim, Al-Ali, Abdulla, Reimann, Thomas and Alinier, Guillaume (2022) Fuzzy Identification-Based Encryption for healthcare user face authentication. Journal of Emergency Medicine, Trauma and Acute Care, 2022 (1). ISSN 1999-7086

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Background: 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.

Item Type: Article
Uncontrolled Keywords: Fuzzy Identity based Encryption, Deep Learning, Face biometry, user authentication, Internet of Medical Things
Subjects: B800 Medical Technology
Department: Faculties > Health and Life Sciences > Nursing, Midwifery and Health
Depositing User: Rachel Branson
Date Deposited: 28 Jan 2022 15:05
Last Modified: 28 Jan 2022 15:15
URI: http://nrl.northumbria.ac.uk/id/eprint/48288

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