Search Results (987)

Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single points of failure, and an absence of patient consent over their data. Methods: This paper describes a novel EMR Gateway system that uses blockchain technology to exchange electronic medical records electronically, overcome the limitations of current centralized systems for sharing EMR, and leverage decentralization to enhance resilience, data privacy, and patient autonomy. Our proposed system is built on two interconnected blockchains: a Decentralized Identity Blockchain (DID-Chain) based on Ethereum for managing user identities via smart contracts, and an Electronic Medical Record Blockchain (EMR-Chain) implemented on Hyperledger Fabric to handle medical record indexes and fine-grained access control. To address the dual requirements of cross-platform data exchange and patient privacy, the system was developed based on the Fast Healthcare Interoperability Resources (FHIR) standard, incorporating stringent de-identification protocols. Our system is built using the FHIR standard. Think of it as a common language that lets different healthcare systems talk to each other without confusion. Plus, we are very serious about patient privacy and remove all personal details from the data to keep it confidential. When we tested its performance, the system handled things well. It can take in about 40 transactions every second and pull out data faster, at around 49 per second. To give you some perspective, this is far more than what the average hospital in Taiwan dealt with back in 2018. This shows our system is very solid and more than ready to handle even bigger workloads in the future. Full article

Wireless optical communication technology offers advantages, such as high-data transmission rates, confidentiality, and robust anti-interception capabilities, making it highly promising for cross-sea–air interface communication applications. However, to our knowledge, no studies have been conducted on the spatial transmission efficiency of light after it passes through ocean waves. To address this issue, a seawater-wave–atmosphere model based on Gerstner waves was constructed. Using the Monte Carlo method, the optical power distributions of the laser light passing through the sea–air interface at the first- and second-level sea scales were simulated. The optimal positions for deploying one to three receiving optical systems were analyzed, and a laser communication receiving system was designed. Furthermore, simulations were conducted to determine the optical transmission efficiency of the designed optical receiver system. At the first-level sea scale, the optimal position for a single-point detector was (0°, ±5.61°), whereas those for the two detectors were (0°, ±5.61°) and (0°, ±5.68°). At the second-level sea scale, the optimal position for a single-point detector was (0°, ±3.17°), and the optimal positions for the two detectors were (0°, ±3.1°) and (0°, ±2.98°). Under the designed conditions, the optical transmission efficiency for a single detector at the first- and second-level sea scales was 0.74–0.88%, respectively, while it was 0.79–1.09% in the two-detector case. At the second-level sea scale, the optical transmission efficiency for a single detector was 0.37–2.09% and 0.50–1.97% in the two-detector case. Full article

The widening gap between life expectancy and healthy life years underscores the need for scalable, adaptive, and privacy-conscious healthcare solutions. In this study, we integrate the AMPER (Aim–Measure–Predict–Evaluate–Recommend) framework with Bidirectional Encoder Representations from Transformers (BERT), Automated Machine Learning (AutoML), and privacy-preserving Federated Learning (FL) to deliver personalized hypertension management. Building on sequential data modeling and privacy-preserving AI, we apply this framework to the MIMIC-III dataset, using key variables—gender, age, systolic blood pressure (SBP), and body mass index (BMI)—to forecast future SBP values. Experimental results show that combining BERT with Moving Average (MA) or AutoRegressive Integrated Moving Average (ARIMA) models improves predictive accuracy, and that personalized FL (Per-FedAvg) significantly outperforms local models while maintaining data confidentiality. However, FL performance remains lower than direct data sharing, revealing a trade-off between accuracy and privacy. These findings demonstrate the feasibility of integrating AutoML, advanced sequence modeling, and FL within a structured health management framework. We conclude by discussing theoretical, clinical, and ethical implications, and outline directions for enhancing personalization, multimodal integration, and cross-institutional scalability. Full article

Blockchain is an emerging technology that is being used to create innovative solutions in many areas, including healthcare. Nowadays healthcare systems face challenges, especially with security, trust, and remote data access. As patient records are digitized and medical systems become more interconnected, the risk of sensitive data being exposed to cyber threats has grown. In this evolving time for healthcare, it is important to find a balance between the advantages of new technology and the protection of patient information. The combination of blockchain–InterPlanetary File System technology and conventional electronic health record (EHR) management has the potential to transform the healthcare industry by enhancing data security, interoperability, and transparency. However, a major issue that still exists in traditional healthcare systems is the continuous problem of remote data unavailability. This research examines practical methods for safely accessing patient data from any location at any time, with a special focus on IPFS servers and blockchain technology in addition to group signature encryption. Essential processes like maintaining the confidentiality of medical records and safe data transmission could be made easier by these technologies. Our proposed framework enables secure, remote access to patient data while preserving accessibility, integrity, and confidentiality using Ethereum blockchain, IPFS, and group signature encryption, demonstrating hospital-scale scalability and efficiency. Experiments show predictable throughput reduction with file size (200 → 90 tps), controlled latency growth (90 → 200 ms), and moderate gas increase (85k → 98k), confirming scalability and efficiency under varying healthcare workloads. Unlike prior blockchain–IPFS–encryption frameworks, our system demonstrates hospital-scale feasibility through the practical integration of group signatures, hierarchical key management, and off-chain erasure compliance. This design enables scalable anonymous authentication, immediate blocking of compromised credentials, and efficient key rotation without costly re-encryption. Full article

Modern distributed computing systems and applications with strict privacy requirements demand robust data confidentiality. A primary challenge involves enabling parties to exchange data or perform joint computations. These interactions must avoid revealing private information about the data. Protocols with the obliviousness property, known as oblivious protocols, address this issue. They ensure that no party learns more than necessary. This survey analyzes the security and performance of post-quantum oblivious protocols, with a focus on oblivious transfer and oblivious pseudorandom functions. The evaluation assesses resilience against malicious adversaries in the Universal Composability framework. Efficiency is quantified through communication and computational overhead. It identifies optimal scenarios for these protocols. This paper also surveys related primitives, such as oblivious signatures and data structures, along with their applications. Key findings highlight the inherent trade-offs between computational cost and communication complexity in post-quantum oblivious constructions. Open challenges and future research directions are outlined. Emphasis is placed on quantum-resistant designs and formal security proofs in stronger adversarial models. Full article

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. Full article

The Internet of Things (IoT) has surfaced as a revolutionary technology, enabling ubiquitous connectivity between devices and revolutionizing traditional lifestyles through smart automation. As IoT systems proliferate, securing device-to-device communication and server–client data exchange has become crucial. This paper presents a novel security framework that integrates elliptic curve cryptography (ECC) with artificial neural networks (ANNs) to enhance the Message Queuing Telemetry Transport (MQTT) protocol. Our study evaluated multiple machine learning algorithms, with ANN demonstrating superior performance in anomaly detection and classification. The hybrid approach not only encrypts communications but also employs the optimized ANN model to detect and classify anomalous traffic patterns. The proposed model demonstrates robust security features, successfully identifying and categorizing various attack types with 90.38% accuracy while maintaining message confidentiality through ECC encryption. Notably, this framework retains the lightweight characteristics essential for IoT devices, making it especially relevant for environments where resources are constrained. To our knowledge, this represents the first implementation of an integrated ECC-ANN approach for securing MQTT-based IoT communications, offering a promising solution for next-generation IoT security requirements. Full article

Quantum technology is significantly transforming businesses, organisations, and information systems. It will have a significant impact on accounting and finance, particularly in the context of cybersecurity. It presents both opportunities and risks in maintaining confidentiality and protecting financial data. This study aims to demonstrate the application of quantum technologies in accounting cybersecurity, utilising quantum algorithms and QKD to overcome the limitations of classical computing. The literature review emphasises the vulnerabilities of current accounting cybersecurity to quantum attacks and highlights the necessity for quantum-resistant cryptographic mechanisms. It discusses the risks related to traditional encryption methods within the context of quantum capabilities. This research enhances understanding of how quantum computing can revolutionise accounting cybersecurity by advancing quantum-resistant algorithms and implementing QKD in accounting systems. This study employs the PSALSAR systematic review methodology to ensure thoroughness and rigour. The analysis shows that quantum computing pushes encryption techniques beyond classical limits. Using quantum technologies in accounting reduces data breaches and unauthorised access. This study concludes that quantum-resistant algorithms and quantum key distribution (QKD) are crucial for securing the future of accounting and finance systems. Full article

Social isolation is a critical but understudied concern for people living with HIV (PLHIV), particularly in rural U.S. communities where social visibility is high and access to supportive services is limited. Disclosure of HIV status is often framed as a health-promoting behavior that facilitates engagement with care and access to social support, yet it can also increase vulnerability to exclusion and isolation, especially where confidentiality is difficult to maintain. Using data from a pilot survey of rural PLHIV in the United States ($n=17$), this study examines when disclosure may function adaptively and when it may coincide with a heightened social burden. A Social Isolation Index was constructed from 15 indicators of exclusion across family, community, and institutional domains. Disclosure was measured both by the number of people informed and whether sexual partners were told. Typological methods and Qualitative Comparative Analysis (QCA) were applied to explore how disclosure patterns relate to race, sexual identity, and reported isolation. The results indicate that disclosure is not uniformly protective: several participants who disclosed widely also reported high levels of isolation, with heterosexual and Black participants often reporting a higher cumulative burden. These findings challenge one-size-fits-all assumptions about disclosure in public health messaging and underscore the need for tailored strategies that recognize both disclosure and nondisclosure as potentially adaptive responses in rural and marginalized communities. Full article

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations. Full article

Managing and analyzing data in data lakes for big data environments requires robust protocols to ensure security, scalability, and compliance with privacy regulations. The increasing need to process sensitive data emphasizes the relevance of secure-by-design approaches that integrate encryption techniques and governance frameworks to protect personal and confidential information. This study proposes a protocol that combines the capabilities of Databricks and format-preserving encryption to improve data security and accessibility in data lakes without compromising usability or structure. The protocol was developed using a design science methodology, incorporating findings from a systematic literature review and validated through expert feedback and proof-of-concept experiments in banking environments. The proposed solution integrates multiple layers, data ingestion, persistence, access, and consumption, leveraging the processing capabilities of Databricks and format-preserving encryption to enable secure data management and governance. Validation results indicate the protocol is effectiveness in protecting sensitive data, with promising applicability in regulated industries. This work contributes to addressing key challenges in big data security and lays the groundwork for future developments in data governance and encryption techniques. Full article

This submission is focused on the implementation of a system that acquires data from various types of sensors and securely stores them after encryption on a chip with a reconfigurable architecture. The system has the unique capability of encrypting the input data with a single secret cryptographic key, which is stored only inside the hardware of the system itself, so the key remains unrecognizable upon completion of the system synthesis for any unauthorized user. Being stored as a part of the whole system architecture, the cryptographic key cannot be attained. It is not stored separately on the system RAM or any other supported memory, making the collected data fully protected. The reported work shows a data acquisition system which measures temperature with a high level of precision, transforms it to degrees Celsius, stores the collected data, and transfers them via serial interface when requested. Before storage, the data are encrypted with a 256-bit key, applying the AES algorithm. The data which are stored in the system memory and sent as UART packets towards the main computer do not include the cryptographic key in the data stream, so it is impossible for it to be retrieved from them. We show the flexibility of such kinds of data acquisition systems for sensing different types of signals, emphasizing secure storage and transferring, including data from meteorological sensors or highly confidential or biometrical data. Full article

This study tackles the issue of ensuring secure communications in vehicular ad hoc networks (VANETs) under dynamic eavesdropping threats, where eavesdroppers adaptively reposition to intercept transmissions. We introduce a scheme utilizing a partitioned reconfigurable intelligent surface (RIS) to assist in the joint transmission of confidential signals and artificial noise (AN) from a source station. The RIS is divided into segments: one enhances legitimate signal reflection toward the intended vehicular receiver, while the other directs AN toward eavesdroppers to degrade their reception. To maximize secrecy performance in rapidly changing environments, we introduce a joint optimization framework integrating meta-learning for RIS partitioning and reinforcement learning (RL) for reflection matrix optimization. The meta-learning component rapidly determines the optimal RIS partitioning ratio when encountering new eavesdropping scenarios, leveraging prior experience to adapt with minimal data. Subsequently, RL is employed to dynamically optimize both beamforming vectors as well as RIS reflection coefficients, thereby further improving the security performance. Extensive simulations demonstrate that the suggested approach attain a 28% higher secrecy rate relative to conventional RIS-assisted techniques, along with more rapid convergence compared to traditional deep learning approaches. This framework successfully balances signal enhancement with jamming interference, guaranteeing robust and energy-efficient security in highly dynamic vehicular settings. Full article

In modern industrial control systems (ICSs), communication protocols such as Modbus TCP remain widely used due to their simplicity, interoperability, and real-time performance. However, these communication protocols (e.g., Modbus TCP) were originally designed without security considerations, lacking essential features such as encryption, integrity protection, and authentication. This exposes ICS deployments to severe security threats, including eavesdropping, command injection, and replay attacks, especially when operating over unsecured networks. To address these critical vulnerabilities while preserving the lightweight nature of the protocol, we propose a Modbus TCP security enhancement scheme that integrates ASCON, an NIST-standardized authenticated encryption algorithm, with the CBOR Object Signing and Encryption (COSE) framework. Our design embeds COSE_Encrypt0 structures into Modbus application data, enabling end-to-end confidentiality, integrity, and replay protection without altering the protocol’s semantics or timing behavior. We implement the proposed scheme in C and evaluate it in a simulated embedded environment representative of typical ICS devices. Experimental results show that the solution incurs minimal computational and memory overhead, while providing robust cryptographic guarantees. This work demonstrates a practical pathway for retrofitting legacy ICS protocols with modern lightweight cryptography, enhancing system resilience without compromising compatibility or performance. Full article

Existing Retrieval-Augmented Generation (RAG) systems face challenges in enterprise settings due to limited retrieval scope and data security risks. When relevant internal documents are unavailable, the system struggles to generate accurate and complete responses. Additionally, using closed-source Large Language Models (LLMs) raises concerns about exposing proprietary information. To address these issues, we propose the Secure Multifaceted-RAG (SecMulti-RAG) framework, which retrieves not only from internal documents but also from two supplementary sources: pre-generated expert knowledge for anticipated queries and on-demand external LLM-generated knowledge. To mitigate security risks, we adopt a local open-source generator and selectively utilize external LLMs only when prompts are deemed safe by a filtering mechanism. This approach enhances completeness, prevents data leakage, and reduces costs. In our evaluation on a report generation task in the automotive industry, SecMulti-RAG significantly outperforms traditional RAG—achieving 79.3–91.9% win rates across correctness, richness, and helpfulness in LLM-based evaluation and 56.3–70.4% in human evaluation. This highlights SecMulti-RAG as a practical and secure solution for enterprise RAG. Full article