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

Distorted Fingerprint Verification System

Fingerprint verification is one of the most reliable personal identification methods. Fingerprint matching is affected by non-linear distortion introduced in fingerprint impression during the image acquisition process. This non-linear deformation changes both the position and orientation of minutiae. The proposed system operates in three stages: alignment based fingerprint matching, fuzzy clustering and classifier framework. First, an enhanced input fingerprint image has been aligned with the template fingerprint image and matching score is computed. To improve the performance of the system, a fuzzy clustering based on distance and density has been used to cluster the feature set obtained from the fingerprint matcher. Finally a classifier framework has been developed and found that cost sensitive classifier produces better results. The system has been evaluated on fingerprint database and the experimental result shows that system produces a verification rate of 96%. This system plays an important role in forensic and civilian applications.Biometric, Fingerprints, Distortion, Fuzzy Clustering, Cost Sensitive Classifier

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points