The growing need for real-time, secure, and noise-resilient biomedical data transmission – especially in critical applications like ECG monitoring – exposes the limitations of traditional Digital Signal Processing (DSP) techniques. These methods struggle with asynchronous signal domains and lack deep learning-based security integration. To address these challenges, this study introduces a novel DSP architecture that combines Wiener Filtering with Asynchronous FIFO buffering for effective noise reduction and timing correction during ECG signal preprocessing. To ensure secure data handling without signal degradation, a Sparse Graph Quantum Hamiltonian Generative Adversarial Attention Network (SpQH-GAN), optimized via Hyperbolic Sine Optimization (HySiO), is proposed. This unified framework supports high-fidelity ECG signal regeneration while ensuring real-time performance and data confidentiality. The model achieved a PSNR of 39.7 dB, SSIM of 0.96, and a 98.7% Mean Opinion Score (MOS) agreement with clinicians, demonstrating its clinical reliability. The proposed solution offers a robust and scalable approach for secure, high-quality ECG data processing in asynchronous and noisy environments.

In medium-sized organizations, frequent turnover of first-level support agents can lead to challenges for new agents, who struggle to discover existing and relevant documentation that would help solve user issues due to inexperience. Consequently, these agents escalate tickets to second-level support professionals, increasing their workload. A proactive knowledge discovery and assistance system targeting first-level service desk agents could help by analyzing tickets using a large language model (LLM) and then finding and presenting relevant documentation utilizing Retrieval-Augmented Generation (RAG) techniques. However, when working with cloud-based LLMs on inference tasks involving sensitive information, data sovereignty is compromised, and there is a risk of confidential content from tickets being leaked, as local information is transmitted to the cloud for processing. To address this issue, we constructed a system based on local LLMs so that the operation of the system does not compromise the privacy and confidentiality of ticket content and wiki documentation, keeping all sensitive data on-premise and secure. Our system, Doku-Assist, proactively finds and presents documentation to first-level support agents, thereby assisting with issue resolution without replacing the human agent. It integrates a DokuWiki-derived knowledge base with the ticket system Znuny. For the evaluation of our system, we used artificial tickets, documentation, and customer issues (to address privacy concerns) based on real-world experience. A second-level support agent was tasked with assessing the utility of the developed user interface as well as the documents proactively discovered and presented, concluding that the found documents presented by the Doku-Assist are useful to proactively fill the knowledge gap of new first-level service desk agents. We conclude that data privacy and law compliance can be achieved by utilizing local LLMs.

he article analyzes the legal norms governing the protection of medical confidentiality and personal data in healthcare using the example of the Republic of Kazakhstan (RK) and international experience. The requirements for ensuring data confidentiality, civil liability for their violation, as well as examples from judicial practice are considered. The main attention is paid to comparing national legislation with the norms of the European Union (GDPR) and the US legislation (HIPAA), which allows identifying the strengths and weaknesses of regulation in the RK and suggesting ways to improve legal regulation in this area.

The digitalization of Indonesia’s judiciary has generated new ethical challenges for the legal profession, particularly in relation to access and use of electronic evidence. Key concerns include potential breaches of data confidentiality, manipulation of digital evidence, and the absence of clear ethical standards governing advocates in e-court proceedings. Current procedural law and the advocates’ code of ethics do not adequately address the complexities of electronic evidence, which inevitably raise issues of privacy, cybersecurity, and procedural justice. This study aims to examine the ethical dilemmas faced by lawyers in digital litigation in Indonesia and to formulate both normative and institutional solutions to strengthen professional accountability. Employing normative legal research in statutory, conceptual, and philosophical approaches, supported by case analysis involving evidence, the study highlights a regulatory gap in the advocates’ code of ethics and deficiencies in ethical oversight mechanisms. Missed leading in legal practices, especially in the electronic court, where everyone can access electronic evidence to support the need for regulation to protect electronic evidence in the electronic court. The findings suggest the need for reform through the incorporation of specific standards on electronic evidence within the code of ethics, enhancement of lawyers’ digital literacy, and establishment of technical guidelines by professional organizations and the Supreme Court.

In the United States, grandparents who live with and provide primary care to their grandchildren have emerged as a particularly vulnerable group since the 1990s. Using confidential data from the U.S. Census Bureau and Social Security Administration, this study linked individuals ages 50 years or older from the 2000 census long-form sample to their death records from 2000 to 2019 (weighted N = 64,027,000) and examined the longitudinal association between coresident grandparenting status and mortality for non-Hispanic White, non-Hispanic Black, Hispanic, and Asian individuals. We found consistently higher rates of mortality for White coresident grandparents and lower rates for Asian coresident grandparents, regardless of the duration of primary caregiving, compared to their peers without coresident grandchildren. We also found increased risks of mortality among Hispanic long-term primary caregivers but reduced risks among Black short-term primary caregivers compared to their peers without coresident grandchildren.

Wireless Sensor Networks (WSN) are widely used across various fields. WSN is composed of many low-cost, high-performance, plug-and-play sensor nodes. WSN is used across a wide range of applications. Distributed Denial-of-Service (DDoS) attacks that overwhelm targeted resources, denying access to legitimate users. It prevents web servers from serving resources to clients. One type of DDoS is an HTTP flood attack, in which an attacker targets network resources, such as bandwidth (the amount of data a network can carry) and CPU processing (a central processing unit's ability to compute). The attacker sends multiple HTTP POST requests to the server to transmit data. In addition, the attacker sends multiple HTTP GET requests to retrieve data from the server. Previous work identified HTTP TRACE flood attacks that misuse the HTTP TRACE method. The HTTP TRACE method returns the received HTTP request, thereby exposing sensitive data. These attacks employ static URLs, which degrade the overall performance of the WSN. To address these issues, introduce the proposed method, the Enhanced Deep Spectral Multi-Layer Convolutional Neural Network (EDSMCNN), a deep learning model designed to improve CPU performance, handle multiple URL requests, and predict TRACE attack traffic based on the maximum-weighted features. First, input the HTTP flood attack dataset, which is available online. The initial step is preprocessing: analyzing and preparing data. The datasets are preprocessed to reduce the dimensionality (number of input features) of non-redundant data. Average weightage scaling feature to filter using the spider algorithm (an optimization technique based on social spider behavior) selects relevant features based on Lattice Service Rate Access Values (LSRAV, a metric evaluating service rate in the system) and observes Trace flood Traffic, considering parameters such as URL, protocol, and IP address (unique network identifier). Social spiders compare feature selection patterns with rank results. Next, SoftMax (a mathematical function that converts numbers to probabilities) generates logistic neurons using the Logistic Activation Function (SLAF, an activation mechanism for neural networks) for HTTP POST and GET requests to prevent trace attacks. Compared to standard convolutional methods, the system achieves high efficiency in detecting HTTP-TRACE flooding attacks. Experimental results show the proposed system improves CPU performance and reduces computation time, helping avoid traffic in one or more HTTP requests. In WSN, security is of paramount importance, and the emergence of novel attack vectors poses significant challenges. This abstract highlight a concerning scenario in which sensor nodes are targeted by a TRACE attacker via the injection of a backdoor entry file. Once compromised, the attacker gains unauthorized access to the WSN web server, potentially exposing sensitive data or gaining control over the network. This sophisticated attack underscores the need for robust security measures in WSNs, including intrusion detection systems, encryption, and authentication protocols, to safeguard against such threats and ensure the integrity and confidentiality of data transmitted and collected within these networks. Developing effective countermeasures to address these emerging attack vectors is crucial for the continued deployment and reliability of WSN in various critical applications.

Aim . The work aimed to determine the applicability of interdisciplinary artificial intelligence (AI) methods, developed and tested by the authors in various domains, to objectives of modern medical diagnostics, as well as to identify the potential for translating these methods into clinical practice, taking into account the methodological, ethical, regulatory, and managerial aspects of their implementation. It also work aimed to highlight the key socioeconomic effects of using AI technologies in medical diagnostics in the context of the national healthcare system. Methods . A literature review on the integration of AI into management and healthcare was conducted, including the authors’ research and a number of relevant international and Russian sources on the use of AI in medical diagnostics. A systems approach and risk-based analysis were used, allowing for a comprehensive consideration of technical and socioeconomic aspects. Objectives . The work seeks to summarize and systematize the interdisciplinary AI methods developed and tested in related fields (public administration, data processing, neurotechnology, ontological decision support systems) in terms of their potential for translation to medical diagnostics. It also seeks to analyze the methodological, ethical, regulatory, and managerial aspects of implementing AI technologies in healthcare diagnostic processes; to assess the potential socioeconomic impact of interdisciplinary AI approaches, including their impact on the accessibility, quality, and effectiveness of medical diagnostics; and to draw generalized conclusions about the prospects and limitations of scaling AI solutions in modern high-tech medical diagnostics. Results . Interdisciplinary AI technologies demonstrate high potential for improving the accuracy and speed of diagnostics, streamlining workflows, and reducing costs in healthcare. It was demonstrated that the use of AI can improve clinical outcomes (e.g., through earlier disease detection) and save resources by reducing unnecessary procedures. However, limitations and risks have been identified, namely ethical and legal obstacles, data confidentiality issues, the need for significant investments in infrastructure and personnel training, and the potential for algorithmic bias.Conclusions. Successful translation of AI methods into medical diagnostics requires a comprehensive interdisciplinary approach that takes into account ethical standards and the development of a regulatory framework. Maximization of positive socioeconomic impacts (improving the quality and accessibility of medical care, reducing costs, and developing technological potential) is possible through risk management, ensuring transparency and trust in AI systems, as well as development of intellectual capital in the AI field during its implementation in medical diagnostics.

Online social networks (OSNs) produce large volumes of user-generated data, enabling personalized services but also exposing users to significant privacy risks, a lack of transparency and frequent security breaches. Existing blockchain- and machine learning–based privacy-preservation methods struggle with high computational costs, limited scalability and weak malicious-node detection. To address these gaps, this work proposes a Blockchain-Driven Privacy Preservation Scheme with Progressive Graph Convolutional Networks (BPPS-SPD-PGCN) for secure and efficient protection of personal data in OSNs. The framework integrates Adaptive Two-Stage Unscented Kalman Filtering for data preprocessing, PGCN for malicious-node detection, ARPO for optimizing PGCN weights and Fair Proof-of-Reputation blockchain for secure access control. Two smart contracts (RG-SH and RG-ST) further enhance data confidentiality and storage integrity. Using the Epinions dataset, the proposed technique was evaluated through Accuracy, Precision, Recall, F1-score and Computational Time. The system achieved 99.04% Accuracy, 92.34% Precision, 99.14% Recall and 99.93% F1-score, outperforming PPB-OSN-GCN, HCS-PSC-SVM and BDI-ISPP-CNN. Overall, BPPS-SPD-PGCN provides a more robust, precise and secure privacy-preservation solution for OSNs, offering significant improvements over existing approaches.

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security.

The article is devoted to a comprehensive study of the administrative and legal mechanism for implementing compliance control as an innovative model of interaction between tax authorities and taxpayers in Ukraine. The author has studied the essence and content of the concept of «tax compliance control», which is considered as a multi-faceted administrative category that encompasses not only monitoring compliance with tax legislation, but also a system of preventive measures aimed at identifying and minimizing legal risks even before the offense is committed. A thorough analysis of the legal status of the subjects of such interaction has been conducted, in particular, the limits of discretionary powers of tax authorities during compliance procedures have been determined. Particular attention is paid to methodological approaches to classifying taxpayers by risk level, which allows differentiating the intensity of control measures and ensuring the targeting of state intervention. The article analyzes the procedural boundaries of horizontal monitoring and remote support of large taxpayers, which are key elements of modern compliance control. Based on the analysis, a number of systemic problems were identified that hinder the effective implementation of the compliance model in national practice. It was found that the lack of a clear legislative definition of the term «compliance control» in the Tax Code of Ukraine creates risks of subjectivity and abuse by regulatory authorities. It was found that the procedural unregulated stage of preliminary coordination of tax positions often leads to the emergence of latent conflicts and a decrease in the level of legal certainty for business. It is proven that without proper administrative and legal support for the protection of confidential information that the payer voluntarily provides during compliance procedures, it is impossible to ensure a high level of trust in state institutions and stimulate the economy to emerge from the shadows. Taking into account the analysis, specific and justified ways of improving the administrative legislation of Ukraine in terms of regulating tax control are proposed.

Abstract Steganography has been developed with the aim of establishing secure communication in a completely imperceptible manner. In a steganography system, confidential information is embedded within a carrier medium such as an image, audio, or video in such a way that no discernible change occurs in the apparent content of the medium, and an observer or unauthorized attacker is unable to detect the presence of the hidden message. However, the use of detection algorithms can enable the identification of the hidden message; if the detection rate of the message’s presence exceeds the random guess threshold, the steganography system is practically considered insecure. Information steganography is an approach for transmitting confidential data in the form of a cover object that provides the highest level of security, such that even if unauthorized access to the transmitted medium occurs, it is impossible to extract or prove the existence of the hidden data. From this perspective, the fundamental goal of steganography is not merely content confidentiality but the non-provability of the existence of the embedded message. In recent years, numerous methods for image steganography have been proposed, each vulnerable to various attacks and methods for detecting hidden messages. Accordingly, in this paper, the concepts and fundamentals of steganography are first reviewed, and then the prominent methods in the field of image steganography, with a special focus on approaches based on evolutionary algorithms, are examined from an analytical perspective.

The paper discusses the development and research of an information system for image generation based on modern generative artificial intelligence models with support for feder-ated learning and retraining mechanisms. The proposed system is focused on ensuring effec-tive generation of visual content while maintaining the confidentiality of user data, which is especially relevant in conditions of limited access to centralized data sets.The research analyzes the architecture of the information system, the principles of inte-gration of generative models, as well as approaches to organizing federated learning, in which model parameters are updated on local nodes without transmitting the output data to the central server. Particular attention is paid to methods of retraining models, which allow the system to adapt to new types of images, styles and user requirements during operation.The performance and efficiency of the proposed system are evaluated in terms of the quality of generated images, learning speed and resistance to changes in input data. The re-sults obtained confirm the feasibility of using a federated approach and further training of generative models to create scalable, adaptive, and secure image generation information sys-tems.In the future, it is planned to expand the functionality of the system, including adding full registration and authorization, the ability to use multiple LoRA adapters simultaneously, increasing the number of models available for training and generation, and implementing ad-ditional algorithms for federated learning.

Modern healthcare institutions face growing challenges in managing medical data ef-fectively and ensuring automation of routine processes. Radio Frequency Identification (RFID) technology has proven to be a powerful tool for improving patient identification, re-ducing human error, and streamlining data access and logistics within hospitals. However, limited attention has been given in recent studies to comprehensive integration of RFID sys-tems with existing medical information infrastructures, particularly concerning data protec-tion and architectural design.This paper proposes an efficient client-server architecture for RFID-based medical sys-tems, aimed at ensuring secure and scalable handling of medical records. The research ana-lyzes a range of architectural approaches, including simple local RFID configurations, peer-to-peer networks, and microservice models, outlining their limitations in the healthcare con-text. As a result, a custom client-server model has been developed using a relational database and secure API-based communication between server and multiple client types (web and mo-bile).The proposed architecture supports centralized data storage and processing, ensures integrity and confidentiality of patient information, and enables flexible integration with third-party healthcare platforms. A modular database design is also presented, including pa-tient profiles, medical history, prescriptions, and user management.Comparative analysis demonstrates that the proposed model outperforms traditional architectures in scalability, automation, and data security. By minimizing administrative workload and enhancing access to reliable patient data, the system improves quality of care and opens perspectives for further research and innovation in medical IT solutions.

Abstract: Nowadays in the era of electronic media it is very critical to protect and conceal information. The project makes it better how we conceal confidential information in pictures through a system called image steganography. The idea is to ensure that, the concealed information remains secure and yet the image portrays as being normal and intact to the human eye. We are employing smart algorithms, encryption and the use of technologies such as Python based OpenCV and NumPy to conceal the data in such manner that no intruder can quickly recognize. A web based platform is also a part of the system as the users can transfer images easily as well as embed data and download data as it is secure. The method can be used to achieve high security and decent visual quality, thus, it is applicable in the real world such as encrypted communication or encrypted file sharing. Keywords: Image Steganography, Visual Quality, Data Hiding, Encryption, LSB, Python, OpenCV, Web Application, Steganalysis Resistance, Secure Communication.

The Internet of Medical Things (IoMT) transforms healthcare through interconnected medical devices but faces significant cybersecurity threats, particularly intrusion and exfiltration attacks. Centralized intrusion detection systems (IDSs) require data aggregation, presenting privacy and scalability risks. This paper proposes FedEnsemble-DP, a privacy-aware Federated Learning (FL) framework for decentralized intrusion detection in IoMT networks. The framework integrates three data balancing scenarios (Raw Imbalanced, Local SMOTE, Centralized SMOTE) with Differential Privacy (DP) and Secure Aggregation mechanisms. Extensive experiments on WUSTL-EHMS-2020 and CIC-IoMT-2024 datasets under non-IID settings (Dirichlet α = 0.3) demonstrate that models with strong privacy guarantees (ε = 3.0) frequently match or exceed non-private baselines. Key findings show Local SMOTE with ε = 3.0 achieved 94.60% accuracy and 0.9598 AUC, while Raw Imbalanced with ε = 3.0 attained 94.50% accuracy and 0.9494 AUC. Even with strict privacy (ε = 3.0), these results surpassed the non-private baseline (93.20% accuracy) in the raw scenario. Centralized SMOTE showed effectiveness but introduced training instability. These results indicate that local data balancing combined with calibrated DP noise can yield high detection performance while preserving privacy, effectively bridging security-performance and data confidentiality requirements in distributed healthcare networks.

The integration of sensing capabilities into secure optical transmission systems is emerging as a pivotal strategy for proactive security threat warning and anomaly detection. However, existing integrated sensing and communication (ISAC) schemes primarily focus on resource sharing, often overlooking the intrinsic security of the communication signals themselves. To address this critical gap, we propose and experimentally demonstrate a novel, to our knowledge, integrated secure communication and sensing (ISCAS) scheme based on simultaneous chaotic encryption and vibration detection utilizing a specially designed random envelope fluctuation linear frequency modulation signal. This uniquely designed signal serves a dual purpose: its random envelope facilitates physical layer encryption, while its LFM nature enables high-precision sensing. As a proof-of-principle demonstration, a 28 Gb/s on-off keying (OOK) confidential signal is securely transmitted over a 20 km single-mode fiber link. Simultaneously, a 1 kHz sinusoidal vibration applied at the 18 km point is detected and localized with an error of only 11.45 m. Our work validates an integrated solution that concurrently ensures information confidentiality and channel-sensing capability, paving the way for more resilient and intelligent optical networks.

<p>The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.</p>

A multi-disciplinary approach is considered best practice when supporting athlete mental health and illness within elite sport. However, research is yet to explore how multi-disciplinary teams operate in this area. This study explores how multi-disciplinary team staff understand mental health and illness and how they negotiate the interpersonal dynamics and tensions of a multi-disciplinary approach to supporting athletes. We conducted five focus groups with a total of 19 participants across a range of professions, including: sport psychologists (n=6); coaches (n=4); physiotherapists (n=2); performance lifestyle advisors (n=2); clinical psychologists (n=2); player care managers (n=2); and a doctor (n=1). Eight hours of data were collected, transcribed verbatim, and analysed using reflexive thematic analysis. We constructed three primary themes: 1) an (over)medicalised understanding of athlete mental health concerns; 2) division within multi-disciplinary teams; and 3) tensions when negotiating confidentiality. Staff showed an over-medicalised understanding of mental health and illness and expressed uncertainty in recognising and supporting sub-clinical mental health concerns. Participants spoke of the divide between coaching staff and science and medicine staff and suggested diverging priorities surrounding mental health and performance. Negotiating confidentiality was a challenge for multi-disciplinary teams, with mental health information guarded by some staff, leaving other staff feeling isolated. However, it was acknowledged that some mental health information must be withheld from coaches due to mental illness stigma. Guidance regarding sub-clinical concerns and the handling of confidential mental health information within multi-disciplinary teams should be developed to encourage effective collaboration within sporting organisations.

Sequential federated analysis of early outbreak data applied to incubation period estimation.

Postoperative pain is common after surgery, with a high incidence and risk of becoming chronic. Current multimodal analgesia has drawbacks, including limited efficacy from single agents and opioid side effects and addiction risk. These issues have led to opioid-sparing multimodal analgesia. Transcutaneous auricular vagus nerve stimulation (taVNS) is non-invasive and convenient. Studies have shown it can reduce postoperative pain, improve mood and lower adverse events. However, taVNS lacks a comprehensive evaluation and standardised protocols, so further research is needed to provide reliable evidence. This study strictly adheres to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. To identify suitable randomised controlled trials (RCTs), eight credible databases will be searched, including four English databases (Web of Science, PubMed, Cochrane Central Register of Controlled Trials, EMBASE) and four Chinese databases (China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, Wanfang Database, Chinese Biomedical Literature Database). RevMan V.5.3 will be employed to integrate the retrieved data and conduct meta-analyses. The methodological quality of included RCTs will be evaluated using the Cochrane Risk of Bias Assessment 2.0 tool. Additionally, the Grading of Recommendations, Assessment, Development and Evaluation system will be applied to assess the strength and certainty of the evidence. We will also conduct publication bias analyses, sensitivity analyses and subgroup analyses. No ethical review is required as no private or confidential patient data will be included. Results of this study will be disseminated through a peer-reviewed journal. CRD420251207651.