Quantum computing for energy systems optimization: Challenges and opportunities

A survey on physical unclonable function (PUF)-based security solutions for Internet of Things

Interconnecting “things” and devices that takes the form of wearables, sensors, actuators, mobiles, computers, meters, or even vehicles is a critical requirement for the current era. These inter-networked connections are serving the emerging applications home and building automation, smart cities and infrastructure, smart industries, and smart-everything. However, the security of these connected Internet of things (IoT) plays a centric role with no margin for error. After a review of the relevant, online literature on the topic and after looking at the market trends and developments, one can notice that there are still concerns with regard to security in IoT products and services. This paper is focusing on a survey on IoT security and aims to highlight the most significant problems related to safety and security in the IoT ecosystems. This survey identifies the general threat and attack vectors against IoT devices while highlighting the flaws and weak points that can lead to breaching the security. Furthermore, this paper presents solutions for remediation of the compromised security, as well as methods for risk mitigation, with prevention and improvement suggestions.

https://link.springer.com/content/pdf/10.1007/s42979-020-00201-3.pdf

Security Considerations for Internet of Things: A Survey

Increasing operational and security demands changed biometrics by shifting the focus from single to multi-biometrics. Multi-biometrics are mandatory in the current context of large international biometric databases and to accommodate new emerging security demands. Our paper is a comprehensive survey on multi-biometrics, covering two important topics related to the multi-biometric field: fusion methods and security. Fusion is a core requirement in multi-biometric systems, being the method used to combine multiple biometric methods into a single system. The fusion section surveys recent multi-biometric schemes categorized from the perspective of fusion method. The security section is a comprehensive review of current issues, such as sensor spoofing, template security, and biometric encryption. New research trends and open challenges are discussed, such as soft, adaptive contextual-based biometrics. Finally, an implementation blueprint for a multi-biometric system is presented in the form of a list of questions to be answered when designing the system.

The Fall of One, the Rise of Many: A Survey on Multi-Biometric Fusion Methods

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

https://www.mdpi.com/1424-8220/20/7/1822/pdf

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

The aim of this paper is to elucidate the implications
of quantum computing in present cryptography and
to introduce the reader to basic post-quantum algorithms. In
particular the reader can delve into the following subjects: present
cryptographic schemes (symmetric and asymmetric), differences
between quantum and classical computing, challenges in quantum
computing, quantum algorithms (Shor’s and Grover’s), public key
encryption schemes affected, symmetric schemes affected, the impact
on hash functions, and post quantum cryptography. Specifically,
the section of Post-Quantum Cryptography deals with
different quantum key distribution methods and mathematicalbased
solutions, such as the BB84 protocol, lattice-based cryptography,
multivariate-based cryptography, hash-based signatures
and code-based cryptography.

/pdf/the-impact-of-quantum-computing-on-present-cryptography-14hh3k4f9r.pdf

The impact of quantum computing on present cryptography

Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy of individuals and violate data protection laws. As a consequence, data controllers and data processors may be imposed tough penalties for non-compliance that can result even to bankruptcy. In this paper, we discuss the current state of the legal regulations and analyse different data protection and privacy-preserving techniques in the context of big data analysis. In addition, we present and analyse two real-life research projects as case studies dealing with sensitive data and actions for complying with the data regulation laws. We show which types of information might become a privacy risk, the employed privacy-preserving techniques in accordance with the legal requirements, and the influence of these techniques on the data processing phase and the research results.

Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR

The majority of deployed biometric systems today use information from a single biometric technology for verification or identification. Large-scale biometric systems have to address additional demands such as larger population coverage and demographic diversity, varied deployment environment, and more demanding performance requirements. Today's single modality biometric systems are finding it difficult to meet these demands, and a solution is to integrate additional sources of information to strengthen the decision process. A multibiometric system combines information from multiple biometric traits, algorithms, sensors, and other components to make a recognition decision. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. The last 5 years have seen an exponential growth in research and commercialization activities in this area, and this trend is likely to continue. Therefore, here we propose a novel multimodal biometric authentication approach fusing iris and fingerprint traits at score-level. We principally explore the fusion of iris and fingerprint biometrics and their potential application as biometric identifiers. The individual comparison scores obtained from the iris and fingerprints are combined at score-level using a three score normalization techniques (Min-Max, Z-Score, Hyperbolic Tangent) and four score fusion approaches (Minimum Score, Maximum Score Simple Sum and User Weighting). The fused-score is utilized to classify an unknown user into the genuine or impostor.

Multimodal biometric authentication using fingerprint and iris recognition in identity management

/pdf/multimodal-biometric-authentication-using-fingerprint-and-3qfvbhkz4w.pdf

Multimodal Biometric Authentication Using Fingerprint and Iris Recognition in Identity Management

This paper presents PS0, an ontological framework and a methodology for improving physical security and insider threat detection. PS0 can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. In all too many cases, rule-based anomaly detection can detect employee deviations from organizational security policies. In addition, PS0 can be considered a security provenance solution because of its ability to fully reconstruct attack patterns. Provenance graphs can be further analyzed to identify deceptive actions and overcome analytical mistakes that can result in bad decision-making, such as false attribution. Moreover, the information can be used to enrich the available intelligence (about intrusion attempts) that can form use cases to detect and remediate limitations in the system, such as loosely-coupled provenance graphs that in many cases indicate weaknesses in the physical security architecture. Ultimately, validation of the framework through use cases demonstrates and proves that PS0 can improve an organization's security posture in terms of physical security and insider threat detection.

Kamer Vishi

Papers

The impact of quantum computing on present cryptography

Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR

Multimodal biometric authentication using fingerprint and iris recognition in identity management

Multimodal Biometric Authentication Using Fingerprint and Iris Recognition in Identity Management

A framework for data-driven physical security and insider threat detection