FIMBISAE: A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem

Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

I. Introduction

The confidentiality, integrity, and availability of healthcare data from unauthorized access is the biggest challenge in the Internet of Medical Things (IoMT) ecosystem. Due to the severity of the healthcare data breach, various countries have made various protocols and rules. When we mention the term user in the healthcare ecosystem, it does not mean doctor and patient only. The user could be a pharmacist, a medical laboratory technician, a caregiver, a care provider, nurses, an insurance company, a patient, a doctor, and a hospital. Sometimes, it is mandatory to exchange information between different types of users as well as departments. Consequently, there is a need to accurately identifying the registered and rightful user through the advanced authentication scheme for the secured healthcare access management. Authenticating a user in the healthcare ecosystem is not new; one of the most preferred classical ways is login id and password, but it has significant vulnerabilities. This id and its associated password are registered only for the particular user. Anyone who is not an intended or authorized person gets the ID and associated password, then it is a data breach. Therefore, an authentication scheme is needed where unauthorized users can not steal your authentication credential, which would be only used by the person who is intended for it. Biometric-based authentication is the best alternative for it, where each user has unique biological characteristics.