Convolutional Neural Network and Data Augmentation for Behavioral-Based Biometric User Identification

The article examines the implementation of artificial intelligence (AI) technologies for processing biometric data in Ukraine’s criminal proceedings. Emphasis is placed on the potential of AI use for identifying individuals by fingerprints, facial features, voice, and other biometric characteristics, which can enhance the efficiency of investigations and evidence gathering. At the same time, a number of risks are outlined: possible algorithm errors and false matches that could lead to unjustified suspicions or arrests; the issue of reliability and transparency of AI “black box” models, whereby parties in a trial cannot verify how a result was obtained; and privacy threats associated with the mass collection and use of sensitive personal (biometric) data without sufficient legal safeguards. It is determined that current Ukrainian legislation lacks clear provisions on the admissibility of evidence obtained through AI, in particular results of automated facial recognition or other biometric identifications. The need for improving the regulatory framework is substantiated: defining the procedural status of data generated by AI; introducing criteria for verifying their accuracy (through expert assessment, validation of methods, etc.); as well as establishing special procedures for judicial oversight over the use of such technologies at the pre-trial investigation and trial stages. Examples of foreign experience are provided: in some cases U.S. courts have admitted AI-driven analyses (e.g., DNA expert reports) as evidence, whereas other rulings have excluded evidence obtained via facial recognition technologies due to concerns about their reliability and the lack of disclosure of the AI methods. At the European level, a cautious approach is observed: the European Parliament advocates significant restrictions on the use of mass biometric surveillance systems, allowing them only in exceptional situations subject to a court order. It is highlighted that the introduction of AI into criminal justice practice must be accompanied by not only technical, but also legal and ethical safeguards. The conclusion is drawn that a comprehensive approach is required to balance the innovative capabilities of AI with human rights protections – from amending the Criminal Procedure Code to record and evaluate AI-derived evidence to taking into account international standards (GDPR, the EU AI Act) and creating mechanisms of independent oversight. Proper judicial control and tailored legal regulation will minimize risks and ensure the admissibility and objectivity of using AI-processed biometric data in criminal proceedings.

Template-Protection-Techniken stellen eine wichtige Ergänzung zu biometrischen Systemen, um das gewünschte Sicherheitsniveau zu erreichen und die Anforderungen an den Schutz der Privatsphäre zu erfüllen. Diese Techniken ermöglichen Erneuerung und Widerruf der Referenzendaten in biometrischen Systemen. In dieser Arbeit wird ein systematisches Evaluierungsframework entwickelt, das sich an die wesentlichen Kriterien und Anforderungen an biometrische Template-Protection-Verfahren festhält. Seine Anwendbarkeit wird durch die Analyse der verschiedenen Algorithmen demonstriert. Die in dieser Arbeit präsentierte Evaluierung ist grundlegend für eine vollständige Analyse. Darüber hinaus ermöglicht es einen Nachweis der Sicherheit und Einhaltung der Privatsphäre. Es schafft eine Basis für Zertifizierung und Benchmark der Template-Protection-Verfahren.

Background. In recent years, the development of an automated identification process using biometric authentication has been observed, which has a high level of protection, since it allows you to evaluate the physical parameters and characteristics of a particular person. Such access control is more reliable since identifiers cannot be transferred to third parties or duplicated to bypass security systems. Over the past decades, a significant number of systems with biometric identification has been developed, but systems with identification according to the characteristics of the photoplethysmogram still receive little attention. The main task of biometric personality identification using photoplethysmogram is the search and implementation of machine learning methods to determine their belonging to a particular patient.Objective. The purpose of the paper is to develop an algorithm for distinguishing pulse wave iterations using the calculation of the temporal characteristics of photoplethysmograms, such as the maximum amplitude value, variance, mean absolute deviation, Wilson amplitude and the total sum of signal amplitude values.Methods. Based on the study of the temporal characteristics of the photoplethysmogram, an algorithm for distinguishing pulse wave iterations is created, which can be used for further biometric identification of a person using machine-learning methods.Results. The results can be used for further development of automated access control and management systems using biometric identification.Conclusions. Known methods of biometric identification are usually based on the static parameters of a person (the structure of the cornea of the eye, palm, fingerprints, geometry of the auricle, etc.), but have a low level of protection, since using special equipment you can create a copy of the biometric key. Therefore, today, the use of methods based on the parameters of dynamic biometric identification (plethysmogram, cardiogram and others) provides the highest degree of protection, but requires a more accurate software device to isolate and determine common symptoms. The proposed approach to calculating individual parameters of the photoplethysmogram with the aim of their subsequent classification by machine learning methods may be an acceptable solution for patient biometric identification systems.

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption

The past few years saw the world's increased interest in biometric identification of Internet users. Biometric data differ from other identifiers: they are immutable and inalienable from their carriers, so any theft (including as a result of leakage) can lead to irreparable consequences. The aim of the article is to study the legal regulation of online biometric identification and processing of biometric data in the United States of America, China, and the European Union, as well as to assess the Russian legal regulation of this area in the light of world experience. For this, the authors use a comparative legal methodology. Each of the cases selected as research material illustrates one of the three regulatory models the authors identified. Each of the models is based on a certain constitutional concept of establishing a balance between users' private interests, on the one hand, and public interests, on the other. The US regulatory model is based on the establishment of minimum government guarantees of human rights observance in the processing of biometric data, while operators of Internet platforms remain with wide discretion in determining the procedure for processing biometric information. The Asian model, implemented, in particular, in China, proceeds from the priority of public interests over the privacy interests of individuals. States that implement such a model in their national legislation form centralized databases of subjects' biometric data, actively involving private Internet service providers in this process. The European model, which is based on the General Data Protection Regulation adopted in the European Union, on the contrary, proceeds from the recognition of the priority of the interests of the individual over public interests, establishing for this purpose strict rules regarding the processing of biometric data by Internet service providers and making it almost impossible to create centralized databases for accumulation of such information. Analyzing the trends in Russian legislation, the authors come to the conclusion that legal regulation in Russia tends to the Asian model and note that its implementation is associated with significant risks to human rights and freedoms, including in connection with data leaks from biometric databases. The emphasis is made on the fact that Russia, unlike Asian countries, is a member of the Council of Europe and is bound by the human rights standards laid down in its international documents. These standards should be taken into account when legislating in this area.

Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds potential for unveiling obscure patterns. Although this hybrid approach extends the class of efficiently solvable problems compared to using only classical computers, this approach has been only realized for solving restricted problems because of the prevalence of noise in current quantum computers. Here, we extend the applicability of the hybrid approach to problems of interest in many-body physics, such as predicting the properties of the ground state of a given Hamiltonian and classifying quantum phases. By performing experiments with various error-reducing procedures on superconducting quantum hardware with 127 qubits, we managed to acquire refined data from the quantum computer. This enabled us to demonstrate the successful implementation of theoretically suggested classical ML algorithms for systems with up to 44 qubits. Our results verify the scalability and effectiveness of the classical ML algorithms for processing quantum experimental data.

Development of biometric human identification algorithms and systems is reviewed. A crisis that prevents them from being used in large computing systems is demonstrated. Comprehensive reliable tests are considered as a way to overcome the crisis. Ways to take into account the influence various factors may have on the reliability of evaluating the practical efficiency of remote human identification algorithms and systems are addressed. A method to determine and calculate quantitative quality criteria of multimodal biometric data is proposed; the possibility of extrapolating the results of testing biometric algorithms to practical application is discussed. Quality functions and the artificiality of biometric data given as the measure of proximity to biometric data registered from an unaware recognition subject are studied.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Computationally efficient, accurate, and privacy-preserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide. In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the biometric probe templates, as well as the stored reference templates and the created index is carried out using homomorphic encryption. The proposed method is extensively evaluated in closed-set and open-set identification scenarios on publicly available databases using two state-of-the-art open-source face recognition systems. With respect to a typical baseline algorithm utilising an exhaustive search-based retrieval algorithm, the proposed method enables a reduction of the computational workload associated with a biometric identification transaction by 90%, while simultaneously suffering no degradation of the biometric performance. Furthermore, by facilitating a seamless integration of template protection with open-source homomorphic encryption libraries, the proposed method guarantees unlinkability, irreversibility, and renewability of the protected biometric data.

Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe nonlinear phenomena. We tried to use this technique to improve existing process of stratigraphy and lithology interpretation and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for several geology data stratigraphy and lithology boundaries classification based on geophysics logging data for deposits in Kazakhstan. Correct marking of stratigraphy and lithology from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting (scikit-learn library), k - nearest neighbour (KNN) and XGBoost.

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.

Generation of synthetic data with low-dimensional features for condition monitoring utilizing Generative Adversarial Networks

Biometric identification is a convenient and reliable method in identity authentication. The widespread adoption of biometric identification requires strong privacy protection against possible theft or loss of biometric data. Existing techniques for privacy-preserving biometric identification mainly rely on traditional cryptographic technology such as oblivious transfer and homomorphic encryption, which will incur huge expenses to the system and cannot be applied to large-scale practical applications. For these issues, we propose a biometric identification scheme by constructing zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK). Our scheme not only reduces the communication overhead, which only needs to send 8 constants to the verifier but also can protect the fingerprint template from disclosure. The time complexity of proof generation and proof verification are about O(C) and O x + log C , respectively, and the size of the proof is only 8 constants, where C and x represent the size of the circuit and the public input, respectively. We have implemented the proposed authentication solution on a public data set of fingerprint images and evaluated the performance and security.

Although the consortium blockchains commonly adopt consensus algorithms with Byzantine fault tolerance (such as practical Byzantine fault tolerance (PBFT)), its consensus efficiency will be degraded by the existence of malicious nodes or behaviors. However, the existing researches mainly focus on the detection of malicious behaviors for public blockchains, but are rare about consortium blockchains. In this paper, an anomaly detection model based on machine learning (ML) classification algorithm is proposed for consortium blockchains that adopt PBFT. Besides, a two-stage process is proposed to reduce the resource consumption for anomaly detection. The data needed for proposed model only has two dimensions and is convenient to obtain. The results of experiment show that ML is very effective in anomaly detection for consortium blockchains. Specifically, the algorithms with the highest accuracy are convolutional neural networks (CNN), k-nearest neighbor (KNN) and support vector machines (SVM) in turn. However, KNN and SVM are more suitable because resource consumption of both algorithms are one third of CNN, and the accuracy rates are above 0.9 which is 0.9% lower than CNN.

Although the field of machine learning in health and well-being has experienced significant growth in recent years, the use of sensitive data in these applications is often restricted. While sports and well-being data may appear less sensitive, they are frequently subject to the same standards as health data. Synthetic data generation has emerged as a potential solution to privacy issues, aiming to replicate the properties of real data without disclosing identifiable information. In this study, we generated synthetic recreational runner data using a combined variational autoencoder and neural network model based on baseline measurements and training responses from a three-month training period. We then evaluated the synthetic data by training predictive models and comparing their performance to models trained on real data, with additional metrics measuring statistical similarity and privacy. While some challenges remain, particularly regarding the modeling of rare cases, handling of missing data, and ensuring privacy, our results demonstrate that synthetic data could be used to train predictive models with performance comparable to that of models trained on real data.