Biometric authentication systems must handle large and constantly evolving datasets and maintain high authentication accuracy and scalability. Existing probabilistic data structures like Bloom Filters (BFs) efficiently handle membership testing, but their conventional forms are inherently static and lack tolerance for noisy data. It is also an inherent challenge in biometric systems, where factors such as lighting or camera angle can introduce noise because of these variability. This paper proposes the Dynamic Hierarchical Bloom Filter (DHBF), a novel variant of the Bloom Filter designed to integrate noise tolerance and substring handling capabilities, while simultaneously offering enhanced scalability and dynamic adaptability. The DHBF accommodates fluctuating dataset sizes and mitigates the impact of noisy biometric samples on authentication accuracy by supporting flexible memory allocation. In this paper, experimental validation was performed on a dataset of 30,000 facial images. The results demonstrate that the DHBF achieves 100% authentication accuracy in dynamic operations such as enrollment, querying, insertion, and deletion. Additionally, our experiments demonstrate a reduction (≈ 30%) in storage requirements in terms of storing biometric templates compared to HBFs.

Digital twin technology initially marked its presence in production and engineering, subsequently revolutionizing the healthcare sector with its groundbreaking applications. These include the creation of virtual replicas of patients and medical devices, enabling the formulation of personalized treatment plans. The rise of microcomputing, miniaturized hardware, and advanced machine-to-machine communications has laid the foundation for the Internet-of-Medical Things (IoMT), significantly transforming patient care through remote monitoring and timely diagnostics. Amid these technological strides, this paper offers a systematic review of digital twin technology’s integration within healthcare IoT, underlining its crucial role in promoting personalized medicine and tackling the pressing security challenges inherent in healthcare IoT systems. Focusing solely on the growing field of smart healthcare systems powered by IoT infrastructure, we explore the use of digital twins in digital patient modeling, the lifecycle of smart hospitals, surgical planning, medical devices, the pharmaceutical industry, and the IoMT cyber infrastructure, demonstrating their transformative potential in modern healthcare. Building on these findings, we outline key technical implications and emerging trends, highlight current challenges, and propose future research directions to advance healthcare IoT and its digital twin applications.

Due to the reliance on untrusted supply chain entities, tracking and authentication of Integrated Circuits (ICs) has become crucial to prevent the rapid proliferation of counterfeits. Physically Unclonable Functions (PUFs) can be used for such IC authentication since they generate unique identifiers for individual ICs. However, PUF-generated signatures are often noisy and traditional solutions like Error Correcting Codes (ECC) are expensive and vulnerable to attacks. Moreover, comprehensive PUF-based authentication at multiple locations of the supply chain at any given time suffers from large storage requirements, high query processing time, and security threats. This article proposes a Persistent Hierarchical Bloom Filter (PHBF) to enable fast, storage-efficient and noise-tolerant authentication to track ICs across the supply chain. The proposed framework is demonstrated using 4,000 PUF-generated signatures from several FPGAs and achieved the highest possible authentication accuracy under temperature-induced and synthetic noise of varied degrees without any ECC. Our comparative analysis of storage and query time requirements against four different solutions for detecting wide range counterfeit ICs shows the significant benefit of PHBF, providing up to \(10^{5}\) times faster query processing and 39 times lower storage requirement compared to blockchain.

This study presents an end-to-end deep learning framework for protein-to-metal-ion binding prediction, a critical task in understanding protein function, structural stability, and metal transport mechanisms. A binding site is a residue location in a protein sequence where a metal binds to a protein. Manual curation of metal binding sites is a tedious process involving mining through research articles, making it expensive, laborious, and time-consuming. Therefore, developing a computational pipeline is essential to predict metal ion binding of unannotated proteins. A significant shortcoming of existing computational methods is the failure to capture the long-term dependency of the residues, the absence of positional information, and a pre-determined set of residues and metal ions. In this paper, we propose a metal-ion binding prediction pipeline using a large language model, emphasizing 1) the comparative performance of five state-of-the-art protein language models (pLMs), 2) the impact of positional encoding of binding sites, and 3) the comparison with classical machine learning techniques. A 10-fold cross-validation evaluation yielded a Matthews Correlation Coefficient (MCC) of 0.89, along with precision, recall, and F1 scores exceeding 95% for the six most extensively studied metal ions reported in the literature.

Two common attacks against biometric systems are direct (or physical) access and indirect (or logical) access. While most detection techniques focus on the former, often called presentation attacks, that occur at pre-sensor level, the attack surface for indirect access, that takes place post-sensor, is larger. In this paper, an indirect attack in the realm of faces is explored that utilizes a unique soft-biometric feature called ‘Kinship Cues’. Unlike gender and ethnicity, kinship is less explored but powerful; we find that its knowledge can significantly increase the chances of an attacker getting access to a system. Due to lack of kin data in other domains, our attack is only performed against facial biometric systems. Nevertheless, the results underscore the impact of kinship cues and their need to be investigated in other domains such as fingerprint and iris. This kinship artifact boosts the convergence speed of state-of-the-art iterative adaptive Bayesian hill climbing attacks. Further, it is exploited to generate a dictionary of input images, commonly called wolf images, in a novel kinship-based non-iterative indirect attack that we call Kin-Wolf. A classical image fusion technique (morphing) and a deep learning based kinship framework utilizing pre-trained StyleGAN2 are investigated to generate the wolf images. The trade-off between kinship cues and randomization is also studied and a 6× average improvement in attack accuracy is achieved for Kin-Wolf over random probes.

Advances in vision and deep learning have revolutionized feature extraction for face recognition and verification systems, yet, performing kinship verification from such features is still challenging. Ongoing research attempts to imitate a human by identifying features for kinship verification. In this paper, we propose KinfaceNet, a deep learning based kinship feature extractor, capable of extracting kinship features from a single input image independently without requiring its kin pair image. The base model of the method is adopted from face recognition domain which is then transfer learned in the domain of kinship by learning a distance mapping from face images to a compact Euclidean space where distances directly correspond to a measure of kinship similarity. Thus, unlike most of the works in deep learning based kinship domain, the extracted features can be used in many other applications such as image generation and family based clustering, etc. Training is performed by rearranging the data into classes of kin pairs and using a state-of-the-art triplet mining algorithm to address the unbalanced kinship data problem which causes overfitting. Also, one of the major advantages of our framework is that training can be performed on any face feature extractor model pre-trained on large face recognition data, thereby reducing training time by a considerable amount. Comparable verification accuracy is obtained from simple MLP network at only 20th epoch with KinfaceNet features extracted from the Family-In-the-Wild dataset, the largest in the wild kinship dataset available, as well as KinfaceW-I and II datasets.

A Hierarchical Bloom Filter (HBF) -based biometric framework was recently proposed to provide compact storage, noise tolerance, and fast query processing for resource-constrained environments, e.g., Internet of things (IoT). While security and privacy were also touted as features of the HBF, it was not thoroughly evaluated. Compared to the classical BFs, the HBF uses a threshold parameter to make robust authentication decisions when the HBF encounters noise in the biometric input which one would think might lead to security issues. In this paper, the attack vectors that could compromise the HBF security by increasing the false positive authentication of non-members and by leaking soft information about enrolled members are explored. With quantitative analyses, HBF-based biometric system security under these well-defined attack vectors is evaluated and it is concluded that the framework is more difficult to attack than the classical Bloom Filter. Further, experimental results show that soft biometric information is also kept private.