Patients want the assurance that the confidentiality of their records accessed through Electronic Health Records (EHR) are safe. With increasing implementation of EHR for health care, privacy concern remains a barrier that limits patients' favorable judgment of this technology. Sensitive records can be compromised and this represents problems in EHRs, which are considered to be more efficient, less error prone, and of higher availability compared to traditional paper health records. In this article, a session based hierarchical key encryption system was developed that allows patient to have full control over certain nodes of their health records. Health records were organized in a hierarchical structure with records further broken down into subcategories. Cryptography was used to encrypt the health records in their different subcategories. Patients' generate a root keys using Blum Blum Shub Algorithm for pseudorandom number generator from which the session-based subkeys were derived, and only authorize users can access these records within a designated period marked as session. The system development demonstrates one way patients' privacy and security can improve using session based hierarchical key encryption system for EHR.

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Unimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABC-based FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS.