Scalable Algorithms for Power Function Calculations of quantum states in NISQ Era

Quantum computing stands at the vanguard of science, focused on exploiting quantum mechanical phenomena like superposition and entanglement. Its goal is to create innovative computational models that address intricate problems beyond classical computers' capabilities. In the Noisy Intermediate-Scale Quantum (NISQ) era, developing algorithms for nonlinear function calculations on density matrices is of paramount importance. This project endeavors to design scalable algorithms for calculating power functions of mixed quantum states. This study introduces two algorithms based on the Hadamard Test and Gate Set Tomography. Additionally, a comparison of their computational outcomes is offered, accompanied by a meticulous assessment of errors inherent in the Gate Set Tomography based approac

Impact of feature proportion on matching performance of multi-biometric systems

Biometrics as a tool for information security has been used in various applications. Feature-level fusion is widely used in the design of multi-biometric systems due to its advantages in increasing recognition accuracy and security. However, most existing multi-biometric systems that use feature-level fusion assign each biometric trait an equal proportion when combining features from multiple sources. For example, multi-biometric systems with two biometric traits commonly adopt a 50–50 feature proportion setting, which means that fused feature data contains half elements from each biometric modality. In this paper, we investigate the impact of feature proportion on the matching performance of multi-biometric systems. By using a fingerprint and face based multi-biometric system that applies feature-level fusion, we employ a random projection based transformation and a proportion weight factor. By adjusting this weight factor, we show that allocating unequal proportions to features from different biometric traits yields different matching performance. Our experimental results indicate that optimal performance, achieved with unequal feature proportions, could be better than the performance obtained with the commonly used 50–50 feature proportion. Therefore, the impact of feature proportion, which has been ignored by most existing work, should be taken into account and more study is required as to how to make feature proportion allocation benefit the performance of multi-biometric systems

Devices for surface working by plane means of vibration rolling

The analysis of manufacturing and technology accessories for regular micro-relief forming at flat surfaces by vibration rolling is given. The new design of apparatus providing an equal vibration roling forces at all vibration rolling devices is proposed

Biometrics based privacy-preserving authentication and mobile template protection

Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template

A review of multi-factor authentication in the internet of healthcare things

Objective: This review paper aims to evaluate existing solutions in healthcare authentication and provides an insight into the technologies incorporated in Internet of Healthcare Things (IoHT) and multi-factor authentication (MFA) applications for next-generation authentication practices. Our review has two objectives: (a) Review MFA based on the challenges, impact and solutions discussed in the literature; and (b) define the security requirements of the IoHT as an approach to adapting MFA solutions in a healthcare context. Methods: To review the existing literature, we indexed articles from the IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink databases. The search was refined to combinations of ‘authentication’, ‘multi-factor authentication’, ‘Internet of Things authentication’, and ‘medical authentication’ to ensure that the retrieved journal articles and conference papers were relevant to healthcare and Internet of Things-oriented authentication research. Results: The concepts of MFA can be applied to healthcare where security can often be overlooked. The security requirements identified result in stronger methodologies of authentication such as hardware solutions in combination with biometric data to enhance MFA approaches. We identify the key vulnerabilities of weaker approaches to security such as password use against various cyber threats. Cyber threats and MFA solutions are categorised in this paper to facilitate readers’ understanding of them in healthcare domains. Conclusions: We contribute to an understanding of up-to-date MFA approaches and how they can be improved for use in the IoHT. This is achieved by discussing the challenges, benefits, and limitations of current methodologies and recommendations to improve access to eHealth resources through additional layers of security

Integration of biometrics and steganography: A comprehensive review

The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper

A comparison of 2D and 3D Delaunay triangulations for fingerprint authentication

The two-dimensional (2D) Delaunay triangulation-based structure, i.e., Delaunay triangle, has been widely used in fingerprint authentication. However, we also notice the existence of three-dimensional (3D) Delaunay triangulation, which has not been extensively explored. Inspired by this, in this paper, the features of both 2D and 3D Delaunay triangulation-based structures are investigated and the findings show that a 3D Delaunay structure, e.g., Delaunay tetrahedron, can provide more feature types and a larger number of elements than a 2D Delaunay structure, which was expected to provide a higher discriminative capability. However, higher discrimination does not necessarily lead to better performance, especially in biometric applications, when biometric uncertainty is unavoidable. Experimental results show that the biometric uncertainty such as missing or spurious minutiae causes more negative influence on the 3D Delaunay triangulation than that on the 2D Delaunay triangulation in three out of four experimental data sets

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper

A Low-Cost Machine Learning Based Network Intrusion Detection System With Data Privacy Preservation

Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms of privacy violations which may threaten end-user privacy. Therefore, to mitigate risk and preserve a balance between security and privacy, it is imperative to protect user privacy with respect to intrusion data. Moreover, cost is a driver of a machine learning-based NIDS because such systems are increasingly being deployed on resource-limited edge devices. To solve these issues, in this paper we propose a NIDS called PCC-LSM-NIDS that is composed of a Pearson Correlation Coefficient (PCC) based feature selection algorithm and a Least Square Method (LSM) based privacy-preserving algorithm to achieve low-cost intrusion detection while providing privacy preservation for sensitive data. The proposed PCC-LSM-NIDS is tested on the benchmark intrusion database UNSW-NB15, using five popular classifiers. The experimental results show that the proposed PCC-LSM-NIDS offers advantages in terms of less computational time, while offering an appropriate degree of privacy protection