Electronic tools to support medication adherence in patients undergoing solid organ transplantation: A systematic review.

Predicting the risk of stroke is one of the critical problems in healthcare, which necessitates efficient solutions for providing accurate and prompt risk assessments while preserving data confidentiality. This work proposes a new framework using Federated Learning (FL) to combine Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models that are essential in analyzing multimodal data. Implemented in Python, the approach incorporates two datasets: Dataset 1, which consists of Demographic data medical history, and lifestyle data, and the second dataset, which includes the normal condition and the affected stroke condition CT scan images. Imputation of missing values, feature normalization by Min-Max scaling, and handling of imbalanced classes with SMOTE make the data pre-processing procedures exhaustive. In FL architecture three clients –Client A, Client B, and Client C – process a split multimodal dataset containing static and sequential information. Each client independently trains an MLP-GRU model. Each is applied with MLP handling static features from Dataset 1 and GRU handling sequential features from Dataset 2. To update models, Federated Averaging is used on a central server, to create a global model that is then returned to the clients for further refinement. The accuracy of the proposed method averages 99.00% and surpasses other models by 2.5% including CNN, LSTM, Random Forest, and SVM. By enhancing MLP with GRU and applying them to a privacy-preserving FL framework,The study addresses the fragmented use of multimodal medical data, where clinical records and imaging are generally evaluated separately, resulting in inadequate diagnostic support. The strategy integrates complementary modalities to create a more comprehensive perspective of patient health, enhancing healthcare predictive accuracy and decision-making. This incentive is essential for improving computational methods and linking technical advancement with medical objectives like fast diagnosis and therapy planning. The introduction emphasises the therapeutic necessity of harmonising organized and unstructured data to reduce diagnostic ambiguity. A translational approach is used to discuss how multimodal integration might improve clinical workflows, develop collaborative healthcare systems, and support sustainable medical practices. This repeated emphasis links methodological advances to real-world healthcare issues, boosting the study’s academic relevance sets.

Digital communication systems face an ongoing major challenge to protect video information from unauthorized access, tampering, and eavesdropping operations. Standalone steganographic methods face two major drawbacks: restricted data storage capabilities and decreased ability to remain undetectable. This study presents an integrated video security framework that unites adaptive video steganography with hybrid cryptographic encryption and blockchain-enabled verification to protect data confidentiality, robustness, and integrity. The proposed framework consists of three separate modules that work together: StegoVision, Robust Video Steganography with Decoy Extraction, and Blockchain-Enabled Encryption. The StegoVision module uses an adaptive Least-Significant Bit (LSB) based embedding scheme with multi-stage compression to enable high-capacity covert communication. The system uses AES-CBC encryption with Knight's Tour-based adaptive embedding and deception techniques to protect unauthorized extractors from detecting hidden data. The system uses hybrid AES-RSA encryption with IPFS-based decentralized storage to achieve tamper-proof authentication and traceability. The proposed method was tested on the HMDB51 (Human Motion DataBase) benchmark dataset. The StegoVision system achieved 94.1% accuracy through its visual quality preservation during compression and format conversion processes. The decoy-based model achieved 42.6 dB PSNR and 0.981 SSIM during compression and format conversion processes. The blockchain module achieved encryption speeds up to 320 MB/s with near-lossless reconstruction (PSNR > 51.4 dB, SSIM ≈ 0.999) and integrity verification accuracy exceeding 99.9%. A comparative analysis demonstrates that the proposed framework achieves an optimal balance between capacity, imperceptibility, robustness, and verifiability.

The secrecy and security of patients' details are among the biggest concerns in Healthcare Information Systems. The Electronic Patient Records (EPR) data, along with doctors' comments can be embedded inside carrier DICOM Images using the proposed scheme. The confidential information is scattered into different sets, and rather than embedding it in a single DICOM image, it is embedded into multiple carriers for enhanced security. These scans can be used together to hide the confidential patient data using the proposed technique. The prototype steganography scheme is tested utilizing LSB Substitution and dummy secret data is embedded inside DICOM Images. The achieved results are imperceptible to the human visual system (HVS). Performance matrices i.e., PSNR, MSE, RMSE, SSIM, NR-IQA parameters (BRISQUE, NIQE, PIQE), as well as entropy, are calculated for cover and stego images. The proposed scheme has been found to be resilient and computationally secure.

ABSTRACT Data deduplication is a crucial data compression technique for eliminating duplicate copies of repetitive data that reduces the bandwidth usage and storage space from cloud service providers (CSP). Data deduplication in cloud computing gained vast attention in large‐scale storage systems, where the main issue comes with security concerns. As the confidentiality of sensitive data gets reduced by data deduplication, a Deep Learning (DL) model is used for data security. In this research, Squeeze Fused Belief Network (SFBN)‐DeepkeyGen is proposed for secure data storage in the cloud environment. Initially, the file is uploaded by the user to the cloud server, which is then allowed to check deduplication. Here, the secret key is generated by SFBN‐DeepkeyGen and then the tag is generated. If the tag is not available, then the file is encrypted using the Advanced Encryption Standard (AES) algorithm. If the tag is available, then the Proof of Ownership (PoW) is checked to perform data deduplication. Finally, the experimental results revealed that SFBN‐DeepkeyGen achieved minimal encryption time, decryption time, and maximal throughput of 0.187 s, 0.197 s, and 0.817 Mbps.

Abstract Understanding economic water use remains challenging due to limited data availability and confidentiality constraints that restrict the robust assessment of sectoral patterns. This study presents the first publicly available national dataset for Scotland that integrates abstraction and network-supplied water volumes across 81 economic sectors, enabling consistent sectoral water-use assessment and ranking of major contributors to national demand. An integrated, diagnostic analytical approach was applied to capture both short- and long-term water-use behaviour. Short-term analysis captures monthly water-use dynamics, including recurring seasonal patterns and responses to extreme events, while long-term results describe sectoral developments and the drivers shaping them. Sectoral profiles combining these observations with qualitative evidence reveal three dominant driver types: hydro-climatic, economic-structural, and behavioural-operational. These profiles were used to derive indicative sectoral estimates for 2025, reflecting heterogeneous and evolving influences over time. Gross Value Added (GVA) provides contextual information but has only limited explanatory power, underscoring the need for sector-specific interpretation. Differentiated approaches therefore provide a more realistic basis for water-use assessment than uniform assumptions, particularly where demand is heterogeneous. The study delivers a sector-level overview of economic water use in Scotland, supporting more transparent monitoring and more robust interpretation of future demand. The dataset further offers insights into water-intensive activities common across many economies. This driver-based classification offers transferable analytical logic for regions with fragmented water-use data and evolving economic structures, supporting more targeted monitoring and realistic demand assessment.

Despite advancements, phishing remains a dominant force in the cybercrime field. Phishing emails continue to pose a significant threat to cybersecurity by exploiting human vulnerabilities to breach the systems defenses, resulting in financial losses, data theft and reputational damage to both victims and organizations being impersonated. While Machine Learning models are effective at detecting phishing threats, their performance mainly depends on the quality and diversity of the training data. This article presents MeAJOR (Merged email Assets from Joint Open-source Repositories) Corpus, a multi-source phishing email dataset designed to overcome critical limitations in existing resources. It integrates 108,685 samples representing a varied number of phishing tactics and legitimate emails, with several statistical features. Both the body and subject of the email entries are anonymized with standard tokens to ensure data privacy and confidentiality, while preserving the original context for email phishing detection models.

Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensitive information leakage, we present P<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ARTITION</small>GPT, the first LLM-driven approach that combines static analysis with the in-context learning capabilities of large language models (LLMs) to partition smart contracts into critical (privileged) and normal codebases, guided by a few annotated sensitive data variables. We evaluated PARTITIONGPT on 18 annotated smart contracts containing 99 sensitive functions. The results demonstrate that PARTITIONGPT successfully generates <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">compilable</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">verified</i> partitions, achieving a precision of 80% while reducing more than 26% code compared to functionlevel partitioning approach. Furthermore, we evaluated P<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ARTITION</small>GPT on nine real-world manipulation attacks that led to a total loss of 25 million dollars, P<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ARTITION</small>GPT effectively prevents eight cases, highlighting its potential for broad applicability and the necessity for secure program partitioning during smart contract development to diminish manipulation vulnerabilities.

As the backbone of global communication infrastructure, optical fibers carry vast amounts of sensitive data, making them prime targets for eavesdropping attacks that can compromise data confidentiality and national security. However, optical fibers are highly vulnerable to eavesdropping attacks-such as fiber bending or evanescent coupling-which can eavesdrop on data without interrupting the service. Accurate detection of eavesdropping remains a critical challenge, particularly under complex environmental disturbances. Environmental factors such as fluctuation and disturbances can induce signal distortions and polarization variations that mask or mimic eavesdropping characteristics, thereby posing a significant challenge to the training and generalization of machine learning-based classifiers. In this Letter, we propose a sensing framework based on a hybrid gated recurrent unit-convolutional neural network model that jointly analyzes polarization parameters and error vector magnitude. The proposed method enables accurate and robust eavesdropping detection and temporal localization under complex physical environments. Experiments conducted across seven practical scenarios demonstrate that the proposed scheme achieves a detection accuracy of 97.2%. Furthermore, we developed real-time and offline optical fiber monitoring platforms integrating mainstream detection techniques to facilitate comprehensive evaluation and deployment.

Unmanned Aerial Vehicles-based blockchain-inspired Intelligent framework for collaborative intrusion detection

Globally the health industry has grown rapidly and turn out to be problematic day by day. Building a strong and secure healthcare system is challenging and it may lead to systematizing the health marketplace. At present, healthcare organizations can offer their stakeholders better and lower-priced services of implementing the Electronic Health Record (EHR) that rolled out the manual-based systems. This transaction made possible by innovations in ICTs. Data mining has made a big change in computer industries with the support of Data Mining. The value of information can be increased by supporting enormous data volumes with implementation of machine learning algorithms to efficiently use the data. In order to provide regular health services, personal information and health-related data must be recorded. These data are strongly related to user privacy and confidential information. Data impairment may result from improper disclosure, loss of data integrity, or inaccessibility. Data miners should have a fundamental awareness of healthcare information privacy, information security, and network security to minimize the risk of harm to people, their organizations, or themselves. The concepts, elements, and guidelines for controlling the security and privacy of healthcare information utilized for data mining are examined in this paper.

The paper presents an analysis of modern hardware and software solutions aimed at increasing the reliability and safety of information and measurement systems used during tests of armored vehicles on the action of a blast wave. The research focuses on the integration and use of Catman AP, DewesoftX, MATLink and I-SPEED Software Suite 2.0 software complexes for synchronous collection, processing and storage of data under conditions of high dynamic and electromagnetic action. The proposed system architecture ensures real-time data integrity and high-precision synchronization between measurement modules and high-speed video channels. The increase in the number of attacks and system hacking requires an increase in the level of protection, which can be successfully solved on the basis of cryptographic transformations. The use of such an approach will make the developed analysis and forecasting system more secure and efficient. To guarantee the confidentiality and authenticity of data, the paper proposes a cryptographic protection mechanism based on an improved method of group matrix transformation. This approach accelerates encryption and decryption processes, ensuring mathematical stability and resistance to cyber influences. The method combines group and non-group two-operand operations with modulo-two addition, which reduces computational complexity and increases the speed of cryptographic protection during real tests. The results of the study show that the integration of specialized software with advanced cryptographic methods significantly increases the efficiency of experimental data analysis, improves protection against unauthorized access and provides comprehensive monitoring of armored vehicle survivability parameters. The proposed solutions can be implemented in modern military and dual-purpose information and measurement systems, ensuring increased measurement accuracy, operational reliability and cybersecurity during field tests.

The increasing digitization of healthcare systems has resulted in the generation and exchange of large volumes of sensitive medical data, including electronic health records and diagnostic medical images. Protecting this information from unauthorized access and cyber threats has become a critical concern for healthcare organizations worldwide. Traditional encryption techniques such as AES, DES, and RSA provide a certain level of security but face significant limitations when applied to large medical image datasets and real-time healthcare communication systems. To address these challenges, this study proposes a novel encryption framework for securing healthcare data by integrating machine learning techniques with the Rubik's cube algorithm. The proposed system encrypts medical images prior to storage and transmission, ensuring that confidential patient information remains protected from unauthorized access. The Rubik's cube algorithm performs complex pixel permutation and rotation operations across multiple dimensions, while machine learning optimizes the encryption parameters to enhance system efficiency and adaptability. Experimental results demonstrate that the proposed approach achieves strong encryption performance, with entropy values approaching 8, high NPCR and UACI scores, low correlation coefficients between adjacent pixels, and acceptable processing time. The system provides a secure and reliable framework suitable for hospitals, diagnostic centers, telemedicine systems, and cloud-based healthcare platforms where safe storage and transmission of medical images are essential.

In this paper, a novel security framework for industrial internet of things (IIoT) and vehicular networks is proposed, integrating blockchain technology with advanced encryption and data classification mechanisms to enhance data integrity, confidentiality and trustworthiness. The work employed ChaCha20-Poly1305 encryption to safeguard the data transaction to local cluster nodes. A private blockchain gateway then processes the encrypted data, classifying it based on confidentiality levels, and directing storage either to cloud servers or the interplanetary file system (IPFS). To ensure data integrity, a proof of authority consensus mechanism within the blockchain is incorporated, while zero knowledge proof (ZKP) methods are used for authentication and secure data access. Empirical evaluations demonstrate that our framework achieves a data transmission security rate of 97.5%, with an average encryption and decryption latency of 150 milliseconds, significantly improving over traditional methods. The proof of authority consensus mechanism exhibits a transaction validation speed of 300 transactions per second, showcasing enhanced efficiency compared to standard blockchain models. Furthermore, the integration of ZKP challenges results in a 30% reduction in unauthorised access attempts, indicating a substantial improvement in overall security. This work emphasises the need for continuous innovation in addressing the various security issues in IoT, ultimately advancing the operational efficiency and security of these systems.

Wireless Sensor Network (WSN) technology is an essential part of Internet-of-Things (IoT) that includes embedded systems with limited resources that are inherently low-powered. The information transmitted via WSN is easily exposed, compromising data confidentiality, authenticity, and integrity. To secure communication in WSN within an IoT context, security solutions must address three vital requirements: A lightweight design to optimize performance within small MSS/MTU constraints, robust protection to prevent MiTM attacks and wireless sniffing and high energy efficiency to preserve the limited battery life of sensor devices. The proposed Hybrid-Trisec security framework employs a three-tier security model that systematically handles key generation, key management, and data encryption. The hybrid cryptographic scheme covers the combination of the advanced LEACH clustering to build an energy-efficient network structure with Chaos theory, block ciphering, and hashing to provide confidentiality, integrity, and anti-replay protection. This security framework integrates a Logistic Chaotic Algorithm for pseudo-random key generation combined with chaos hash-driven per-packet dynamic session key management, and a Lightweight Feistel Algorithm for block cipher encryption, balancing security strength with resource constraints. Encryption, integrity, authenticity and replay protection are equally enforced while maintaining lightweight computation, memory efficiency, and energy awareness suitable for WSN nodes. The proposed security technique’s effectiveness was benchmarked against the conventional symmetric block cryptography methods such as SPN, Feistel and a “Lightweight Security Algorithm” (LSA). The simulation outcome exhibited that the proposed method enhances threat resilience, reduced key generation time by 0.0073 ms for 256-bits keys, power consumption reduced by 142 µJ and the packet delivery ratio proved to have achieved 100 % for 100 nodes when compared with existing methods.

Background Asynchronous online psychotherapy (AOP) offers a promising solution to improve access to mental healthcare, particularly in times of global crises. However, therapists practicing AOP face challenges such as emotional strain, isolation, and increased workloads. The integration of generative artificial intelligence (GenAI) may provide support, yet little is known about its impact on the empowerment of mental health professionals, specifically regarding their perceived competence and autonomy, and how it can be integrated into blended care environments. Methods A mixed-method study was conducted with thirteen Ukrainian mental health professionals who engaged in both AI-supported and independent psychotherapy tasks using a digital agent, called APIA, powered by GPT-3.5/4. Participants completed standardized tasks such as forming diagnoses and treatment plans and responding to patient inquiries, followed by surveys and semi-structured interviews. Quantitative data were analyzed using descriptive statistics and paired-sample t-tests. Qualitative data from open-ended responses and interviews were analyzed thematically. Results Quantitative findings revealed that interaction with APIA led to a statistically significant increase in perceived competence (p < 0.01) and a marginal increase in perceived autonomy (p < 0.1). Qualitative data supported these findings, indicating that APIA enhanced task efficiency, supported validation of judgments, and improved independence. Three archetypes for integrating digital agents into AOP were identified: psychotherapist-centric, patient-centric, and therapy-centric. Despite the benefits, concerns were raised about data confidentiality, overreliance by inexperienced professionals, and potential depersonalization of care. Conclusions GenAI-based digital agents can positively influence mental health professionals’ sense of competence and, to a lesser extent, autonomy in asynchronous online psychotherapy settings. This empowerment may contribute to improved therapeutic quality and professional well-being. However, thoughtful integration, method-specific constraints, and ethical considerations must be addressed to realize the full potential of AI-supported mental healthcare.

The impact of technology on improving health and well-being of individuals is remarkable. EHealth boosts the transition from paper-based health records to Electronic Health Records (EHRs). The use of EHRs can lead to improve quality of care, costs and time. In eHealth systems the health data is stored in digital form, and can be exchanged or accessed securely by authorised users. It is worth noting that medical data is considered very confidential information. However, the privacy and security of medical data remains a critical issue. Any leak or breach in security can lead to serious privacy damages for patients. Despite the safeguards, training courses and the consciousness on keeping data safe, the human error continues to be a problem. The main purpose of this paper is to present a bibliometric overview on the academic research related to privacy and security in EHRs. For this purpose, the papers of this study were searched in the Scopus. A period of 24 years was considered for selecting the papers. The information gathered in the database identified a total of 3,077 publications. Some key findings revealed that in the year 2015 the highest number of publications was produced. The Harvard Medical School was the most prolific institution with 2.44% papers from the total number of publications. A total of 97.21% of the documents were written in English. Finally, the results provided in this manuscript allowed us to make a picture on the current relevance in academic literature on privacy and security in EHRs.

The ubiquitous deployment of the Internet of Things (IoT) systems in the areas of essential industries, such as industrial automation, healthcare, and intelligent urban infrastructure, have essentially changed the contemporary data exchange. Nevertheless, heterogeneous and decentralized IoT ecosystems pose extreme security risks especially in data confidentiality, authentication of devices, and data provenance. The existing cryptographic primitives (like the Advanced Encryption Standard (AES) and the Rivest Shamir Adleman (RSA)) have computational and energy consumption that is beyond the operational limitations of resource-constrained edge devices. To mitigate these systemic shortcomings, this study proposes a new highly scalable secure communication system, which is symbiotically coupled with lightweight cryptographic algorithms, and permissioned edge-blockchain system. The framework suggested uses the ASCON and SPECK lightweight ciphers to securely encrypt a payload in a highly efficient manner, hybridized with Elliptic Curve Cryptography (ECC) to securely exchange keys with minimal overhead. This cryptographic layer is mathematically anchored to a distributed ledger based on the Hyperledger Fabric and placed at the network edge. The architecture, based on an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, using queuing theory modeling, ensures non-mutability of transactions and autonomous access control through smart contracts without overloading the sensory nodes. Large-scale empirical tests based on NS-3 to model networks, Contiki Cooja to profile constrained devices, and the CICIoT2023 dataset to test network resilience to intrusions show significant performance benefits. The architecture consumes less cryptography power of up to 39.2 mW when using ASCON-128a and the end-to-end transaction latency of up to 87 ms on average, and a throughput of 550 transactions per second. Anomaly detection models built on machine learning and implemented at the edge had an accuracy rate of 99.89 percent in neutralizing advanced vectors prior to ledger committal. The paper adds to the verifiable, low-overhead architecture blueprint to ensure the security of next-generation IoT deployments against changing cyber-physical threats.

The increasing number of Machine-Type Communication (MTC) devices in Long-Term Evolution (LTE) networks has raised concerns about security and authentication during group handovers. This work proposes a BC-based group handover authentication protocol to enhance MTC device security. The protocol utilizes a Chimp Optimization Algorithm (ChOA) to select the optimal relay node with minimal energy consumption, distance, and delay for group handover authentication. Once the optimal relay node is selected, the protocol employs block chain (BC) technology to facilitate secure and decentralized authentication when a device hands over from one node to another within a group of nodes. The BC network ensures the integrity and confidentiality of authentication data, while the COA optimizes relay node selection to reduce energy consumption, delay, and distance. Simulation results demonstrate the protocol’s effectiveness in Network lifetime, reducing authentication delay, and optimizing energy efficiency during group handovers. The proposed protocol achieves a significant reduction in energy consumption, delay, and distance, highlighting its potential for securing MTC devices in LTE networks.

With the accelerating growth of the digital economy, data has emerged as a core asset, making secure and private data trading a pressing necessity. However, traditional centralized data trading platforms face critical challenges, including identity exposure, data leakage, unclear ownership, and lack of trust. Although decentralized, blockchain-based solutions have been proposed, they typically protect only subsets of these properties and seldom provide a unified, verifiable privacy architecture over the entire trading lifecycle. This paper introduces a novel decentralized data trading system that comprehensively integrates Groth16-based zero-knowledge proofs (ZKPs), Merkle tree–based data ownership commitments, and smart contracts on blockchain. The proposed system ensures identity anonymity, data confidentiality, ownership traceability, and behavioral privacy while supporting regulatory auditability. Rather than proposing new cryptographic primitives, we reformulate data trading as a zero-knowledge–verifiable privacy problem and embed the resulting privacy logic into the protocol and contract design. The main contributions are as follows. (1) Developing a unified zero-knowledge privacy layer that combines Groth16-based ZKPs with proxy re-encryption, allowing participants to prove transaction eligibility without disclosing identity attributes while keeping traded data encrypted end-to-end. (2) Constructing a zero-knowledge-based ownership lifecycle in which Merkle trees are repurposed as privacy-preserving ownership commitment structures that support unlinkable ownership proof, secure ownership transfer, and privacy-preserving traceability. (3) Designing a malleability-aware ZKP execution framework for Groth16 proofs, implemented via dedicated “anti-malleability” contracts that bind proofs to ownership states, fresh randomness, and protocol stages, thereby mitigating proof malleability and unsafe reuse across the registration–sale–transfer lifecycle. (4) Integrating a trusted regulatory authority into the architecture to enable compliant yet anonymous audits and formulate a system-wide privacy framework covering identity, data, ownership, behavioral, and audit dimensions. Experimental results demonstrate that the system achieves strong privacy guarantees and low on-chain overhead, offering a more robust and privacy-centric approach to data transactions than existing solutions.