In secure sensor-based applications, generating unique and secure credentials is crucial for ensuring trust and privacy. Traditionally, pseudo-random number generators have been used for this purpose. However, biometric data, especially fingerprint features, has emerged as a robust alternative. This article proposes a dynamic approach for generating a privacy-preserving unique identity from fingerprint sensor data. The method isolates a region of interest (ROI) from the fingerprint image and extracts two feature sets: minutiae-based data and texture-based information. These features are combined and optimized to produce a hybrid feature vector containing discriminative and relevant attributes. A unique identity (termed a key) is then dynamically generated from this vector, characterized by reliability, revocability, unlinkability, and irreversibility. Extensive evaluations using fingerprint images from sensors of varying quality and resolution have demonstrated the method’s robustness. The generated identities have been statistically validated using NIST and Diehard test suites, confirming strong adherence to randomness requirements. Also, comprehensive security analyses have shown resilience against different adversarial attacks. Notably, the approach avoids storing biometric data or generated identities, enhancing privacy protection. These features make the proposed method ideal for secure sensor-based applications such as authentication, data storage security, and digital signature schemes.

Performance of biometric systems is highly dependent on the quality of the input samples captured by the sensing device. Although measures are taken for capturing high quality images, but the authentication system mandates the analysis of captured images for selection of precise data. The benefit of such an analysis are two-fold; it helps to identify the best sample, and is useful for improving the sensor design, user interface for sample collection and providing data interchange standards. In this work, we propose to analyse the quality of the sample data by using a two-stage fuzzy quality evaluation system. The proposed work has been demonstrated on the iris images using CASIA - 3.0 Interval, CASIA 4.0 Interval and IIT Delhi iris database. We evaluate the -quality of the images by classifying them into classes. The experimental results verify the efficacy of the proposed method.

To guide the researcher in searching right paper in the context of an increasing rate of the digital repository of research articles, research paper recommendation system is being advocated highly. To design such a system, crawling, filtering and ranking are the three crucial steps. For the crawling task, the existing approaches consider only direct features, which can be readily obtained from a given paper. Addressing this limitation, this work proposes a novel scheme to define the feature vector representing an article. The proposed work unleashes three hidden features that can not be readily available for a given paper. The three indirect features derived from the direct features itself (which are readily available from a paper) are based on the measurements of keyword diversification, text complexity and citation analysis over time. The rationale behind the proposition of the three indirect features, metrics and their measurements are discussed in detail in this paper. Experimental results clearly substantiate the efficacy of the proposed feature vector.

In this paper, we introduce an online facial expression recognition (FER) model, which infers the emotional states in real time. This model enables the computer to interact more intelligently with the user. Our proposed mechanism identifies the frontal face along with the region of interest (ROI), extracts discriminating features from suitable facial landmarks, and classifies the facial expressions. Histogram of oriented gradient (HOG) is implemented to extract features and facial landmark positions from active facial regions, which enhances the system performance against all the possible scale and pose variations. The system speed improved with appropriate integration of detection and tracking algorithms. Further, support vector machine (SVM) classifier is used to classify the detected face into neutral or six universal emotions. For achieving the best results with new user faces, the system extracts the neutral features of the user during the time of execution and uses them to train the classifiers. To validate the performance of the proposed algorithm is validated using CK+ and RafD databases.

Password-based Authentication System (PAS) is one of the preferable solutions to access secure cloud services via Internet applications. In this regard, four well-known PAS systems viz; single server, multiserver, gateway augmented multiserver and two-server based PASs have been evolved in the existing state of the art. To assess dependable user authentication services in the cloud, high server-side availability of the aforesaid systems are necessary. In this paper, we propose a unified mathematical model for both single server and two-server based PAS systems. We put forward a Generalized Stochastic Petri Nets (GSPNs) based system modeling and Markovian state-based dependability analysis technique to quantify two important dependability metrics viz; reliability and availability for the said PAS systems. The performance analysis substantiates that the two-server based PAS is more dependable than the single server-based PAS in terms of availability. The proposed approach can be useful in the context of emerging cloud computing paradigm for measuring the security service dependability.

In recent years, biometric applications have significantly gained popularity. Such applications involve voluminous databases of high dimensional data. These enormous databases increase the cost of identification and degrade the system performance. To resolve such an issue a plethora of algorithms based on geometric hashing, k–d tree, k ‐means clustering, etc., have been proposed in the literature. Although, these algorithms solve a number of concomitant challenges of multi‐dimensional data, yet, they fail to present a universal solution. In this study, we propose an indexing mechanism, which partitions the data space effectively into zones and blocks using a set of hash functions. Furthermore, the index locations are divided into maximum nine sub‐locations to store data. This helps in carrying out an efficient search of the queried data, thereby minimising the false acceptance and rejection rate. To validate the proposed approach, the mechanism has been applied to the fingerprint verification competition and National Institute of Standards and Technology fingerprint image databases. The experimental results substantiate the efficacy of our approach in terms of accuracy, speed, reduction of search space and the number of comparisons to store and retrieve data.

For a secure communication over an insecure channel the Diffie-Hellman key exchange protocol (DHKEP) is treated as the de facto standard. However, it suffers form server-side compromisation, identity compromisation, man-in-the-middle, replay attacks, etc. Also, there are single point of vulnerability (SOV), single point of failure (SOF) and user privacy preservation issues. This work proposes an identity-based two-server DHKEP to address the aforesaid issues and alleviating the attacks. To preserve user identity from outside intruders, a k-anonymity based identity hiding principle has been adopted. Further, to ensure efficient utilization of channel bandwidth, the proposed scheme employs elliptic curve cryptography. The security analysis substantiate that our scheme is provably secure and successfully addressed the above-mentioned issues. The performance study contemplates that the overhead of the protocol is reasonable and comparable with other schemes.

Abstract This paper presents an approach to generate test cases from UML 2.0 sequence diagrams and subsequently prioritize those test cases using model information encapsulated in the sequence diagrams. The test cases generated according to the proposed approach satisfy the scenario coverage criterion and are suitable for system‐level testing. For prioritizing test cases, three different prioritization metrics are proposed. The values of these prioritization metrics can be analytically computed from the model information only. This paper also presents an approach to generate test data using a concept called rule‐based matrix. The prioritization metrics are used to control the number of test data without compromising the test adequacy. The effectiveness of the proposed approach has been verified using two industrial designs. Copyright © 2009 John Wiley & Sons, Ltd.

Coverage of system states during system testing is a nontrivial problem. It is because the number of system states is usually very large, and system developers often do not construct system state model. In this paper, we propose a method to design system test cases to achieve coverage of system states based on UML models constructed during normal development process. We use UML use case, sequence and class level statechart models to generate a set of sequences of scenarios that can achieve adequate coverage of system states.