Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography

Several recent works have proposed and implemented cryptography as a means to preserve privacy and security of patients health data. Nevertheless, the weakest point of electronic health record (EHR) systems that relied on these cryptographic schemes is key management. Thus, this paper presents the development of privacy and security system for cryptography-based-EHR by taking advantage of the uniqueness of fingerprint and iris characteristic features to secure cryptographic keys in a bio-cryptography framework. The results of the system evaluation showed significant improvements in terms of time efficiency of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the likelihood of imposters gaining successful access to the keys protecting patients protected health information. This result also justifies the feasibility of implementing fuzzy key binding scheme in real applications, especially fuzzy vault which demonstrated a better performance during key reconstruction

Performance Evaluation of Geometric Active Contour (GAC) and Enhanced Geometric Active Contour Segmentation Model (ENGAC) for Medical Image Segmentation

Segmentation is an aspect of computer vision that deals with partitioning of an image into homogeneneous region. Medical image segmentation is an indispensable tool for medical image diagnoses. Geometric active contour (GAC) segmentation is one of the outstanding model used in machine learning community to solve the problem of medical image segmentation. However, It has problem of deviation from the true outline of the target feature and it generates spurious edge caused by noise that normally stop the evolution of the surface to be extracted. In this paper, enhanced Geometric active contour was formulated by using Kernel Principal Component Analysis(KPCA) with the existing Geometric active contour segmentation model and performance evaluation of the formulated model was carried out. Keyword: Geometric active contour, Segmentation, Medical image, Kernel Principal Component Analysis

Exploiting Multimodal Biometrics in E-Privacy Scheme for Electronic Health Records

Existing approaches to protect the privacy of Electronic Health Records (EHR) are either insufficient for existing medical laws or they are too restrictive in their usage. For example, smartcard-based encryption systems require the patient to be always present to authorize access to medical records. A major issue in EHR is how patient’s privacy and confidentiality can be maintained because there are known scenarios where patients’ health data have been abused and misused by those seeking to gain selfish interest from it. Another issue in EHR is how to provide adequate treatment and have access to the necessary information especially in pre-hospital care settings. Questionnaires were administered by 50 medical practitioners to identify and categorize different EHR attributes. The system was implemented using multimodal biometrics (fingerprint and iris) of patients to access patient record in pre-hospital care. The software development tools employed were JAVA and MySQL database. The system provides applicable security when patients’ records are shared either with other practitioners, employers, organizations or research institutes. The result of the system evaluation shows that the average response time of 6seconds and 11.1 seconds for fingerprint and iris respectively after ten different simulations. The system protects privacy and confidentiality by limiting the amount of data exposed to users. The system also enables emergency medical technicians to gain easy and reliable access to necessary attributes of patients’ EHR while still maintaining the privacy and confidentiality of the data using the patient’s fingerprint and iris. Keywords: Electronic Health Record, Privacy, Biometric

Securing Private Keys in Electronic Health Records Using Session-Based Hierarchical Key Encryption

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