Ensure Data Security Research Articles

A consortium blockchain-edge enabled authentication scheme for underwater acoustic network (UAN)

The Internet of Things (IoT) allows for automated operations in diverse fields, such as agriculture monitoring, pollution monitoring, health care, and underwater monitoring. The Internet of Underwater Things (IoUT) observes the underwater environment, assists in exploration, mitigates disasters, and monitors some factors including temperature, pressure, and pollution. The IoUT relies on a network of intelligent underwater sensors that send data to surface base stations and IoT devices for storage and analysis. Nevertheless, these systems face security risks as they operate in unattended environments. Many authentication methods depend on a centralized third party, leading to higher computation costs and energy usage. To mitigate security risks, autonomous underwater devices need secure connections and authentication. This paper suggests a decentralized authentication mechanism for UAN to safeguard against unauthorized access and ensure secure data storage in the cloud. The proposed mechanism prioritizes robustness, transparency, and energy efficiency. The suggested solution incorporates an architecture based on edge and cloud layers, utilizing customized blockchain technology for secure storage and processing of data. The security of the proposed solution has been thoroughly examined through formal analysis utilizing the Real or Random (ROR) Oracle model and Scyther tool. Informal analysis further confirms the solution’s resilience against various malicious attacks. Additionally, performance and comparative analysis demonstrate that the proposed solution surpasses existing schemes.

Utilizing digital footprint analysis for end-to-end risk-based authentication in medical billing systems

Healthcare systems face escalating challenges in securing sensitive financial and patient data due to sophisticated fraud tactics and unauthorized access. This study presents a dynamic risk-based authentication (RBA) framework that leverages digital footprint analysis, including behavior monitoring, device recognition, and location-based anomaly detection, to strengthen security. The framework utilizes machine learning models, Isolation Forest for outlier detection, and recurrent neural networks for sequential behavior analysis, to assess risk in real-time, dynamically adjusting authentication requirements based on risk profiles. Privacy-preserving technologies such as homomorphic encryption and federated learning are integrated to comply with HIPAA and GDPR standards, ensuring secure data handling without centralization. Findings show that the proposed RBA framework effectively reduces false positives, improves detection accuracy, and provides a scalable solution for securing medical billing systems. This adaptive approach supports both user experience and stringent privacy compliance, laying the groundwork for more resilient healthcare data security systems. Future studies could extend this framework by incorporating blockchain to enhance data transparency and auditability across transactions.

Privacy-Preserving Modeling of Trajectory Data: Secure Sharing Solutions for Trajectory Data Based on Granular Computing

Trajectory data are embedded within driving paths, GPS positioning systems, and mobile signaling information. A vast amount of trajectory data play a crucial role in the development of smart cities. However, these trajectory data contain a significant amount of sensitive user information, which poses a substantial threat to personal privacy. In this work, we have constructed an internal secure information granule model based on differential privacy to ensure the secure sharing and analysis of trajectory data. This model deeply integrates granular computing with differential privacy, addressing the issue of privacy leakage during the sharing of trajectory data. We introduce the Laplace mechanism during the granulation of information granules to ensure data security, and the flexibility at the granularity level provides a solid foundation for subsequent data analysis. Meanwhile, this work demonstrates the practical applications of the solution for the secure sharing of trajectory data. It integrates trajectory data with economic data using the Takagi–Sugeno fuzzy rule model to fit and predict regional economies, thereby verifying the feasibility of the granular computing model based on differential privacy and ensuring the privacy and security of users’ trajectory information. The experimental results show that the information granule model based on differential privacy can more effectively enable data analysis.

Blockchain in Healthcare: Ensuring Data Security and Integrity

Blockchain in Healthcare: Ensuring Data Security and Integrity

Design and Performance of Privacy Protection Model for Big Data Transmission Based on Mixed Encryption

With the advent of the big data era, data security and privacy protection have become particularly important. Big data has advantages such as large scale and diverse types, but it also brings risks of personal privacy leakage and data abuse. However, the symmetric or asymmetric encryption techniques alone have limitations in big data security and privacy protection. Therefore, a privacy protection model for big data transmission based on mixed encryption is proposed and experimentally validated. The research results indicated that the asymmetric encryption algorithm used had an encryption time of less than 20 ms, and the key space occupation was only 0.031 Kb to 0.063 Kb. After improving the symmetric encryption algorithm, it achieved a lower correlation of 0.16 within 18 ms and increased the number of ciphertext transformations to an average of 82 bits. In the performance verification of mixed encryption technology for 60 MB data packets, the proposed mixed encryption technology took 273.1 ms, the decryption took 254.7 ms, the correlation was as low as 0.12, and the average resistance time in resisting violent attacks exceeded 100 s. The model proposed in the study improves the encryption and decryption speed while ensuring data security, which has important practical application value and theoretical significance for data privacy protection in big data environments.

Enhancing Cloud Security: Strategies and Technologies for Protecting Data in Cloud Environments

With the rapid adoption of cloud computing, ensuring the security of data has become a paramount concern for organizations of all sizes. This paper explores various strategies and technologies for protecting data in cloud environments. Encryption emerges as a fundamental technique for safeguarding data both at rest and in transit, with block ciphers and stream ciphers offering robust encryption methods. Additionally, hash functions play a critical role in verifying data integrity and ensuring secure data storage. We delve into the implementation of these techniques in cloud environments, highlighting best practices for encryption, key management, and data integrity verification. Moreover, we address common cloud vulnerabilities, such as lack of appropriate governance, isolation failure, and malicious attacks, and discuss mitigation strategies to strengthen cloud security posture. By leveraging encryption, block ciphers, stream ciphers, and hash functions, organizations can enhance data protection in the cloud and mitigate the risks associated with unauthorized access, data breaches, and tampering. This paper serves as a comprehensive guide for organizations seeking to enhance their cloud security and maintain trust in cloud-based services.

Advanced Techniques in Post-Quantum Cryptography for Ensuring Data Security in the Quantum Era

The arrival of quantum computers is a major threat to current security methods, which depend on problems like discrete logarithms and integer factorization being hard. Post-quantum cryptography (PQC) is getting to be an critical field for making secure strategies that can't be broken by quantum assaults. This exposition talks almost progressed PQC strategies, centered on the most up to date thoughts and what they cruel for securing information within the quantum age. To begin with, we see at lattice-based cryptography, which appears like a great choice since it has solid security roots and can be utilized in numerous circumstances. This incorporates strategies such as Learning With Mistakes (LWE) and Ring-LWE, which are exceptionally great at ensuring against quantum dangers and are simple to utilize. Another critical zone is code-based cryptography, which employments the trouble of breaking irregular straight codes as an case. The McEliece cryptosystem is one such framework. It is checked to see how valuable and successful these strategies are in real-life circumstances. We see into multivariate polynomial encryption, which builds security on the truth that it's difficult to illuminate sets of multivariate quadratic equations. People are particularly curious about this strategy since it may be utilized to form proficient marking plans. Another imperative strategy is hash-based cryptography, which employments hash capacities to create secure advanced marks. The consider moreover talks around blended cryptography frameworks that utilize both conventional and post-quantum strategies. These frameworks make beyond any doubt that the switch to quantum computing goes easily and give way better security. We see at the issues that come up with implementation, like additional work that has to be done on the computer and joining it with current frameworks, and come up with ways to urge around these issues. At long last, moving to post-quantum security is necessary, but it comes with a part of problems that ought to be illuminated in numerous ways.

Document Management System

A Document Management System (DMS) is a digital solution that enables the efficient organization, storage, retrieval, and management of documents in electronic format. It aims to streamline workflows, enhance productivity, and ensure data security in businesses or organizations by digitizing document handling processes. With features like version control, secure access, metadata tagging, and automated workflows, DMS minimizes the dependency on physical paper, reduces the risk of document loss, and facilitates compliance with regulatory standards. Additionally, a DMS enables remote access and collaboration, making it easier for team members to work together on documents in real time, regardless of location. By centralizing document management, a DMS enhances operational efficiency, optimizes resource utilization, and supports the digital transformation efforts of modern enterprises.

The Role of Artificial Intelligence in Revolutionizing Mental Health Services: A Data-Driven Approach

The potential of artificial intelligence (AI) to transform mental health care through individualized treatment approaches is significant. This research investigates the application of AI and extensive healthcare datasets to improve the precision of mental health diagnoses and treatments. The study employs various machine learning techniques, such as Random Forest, CatBoost, K-Nearest Neighbors, XGBoost, Convolutional Neural Networks, and Long Short-Term Memory networks, to analyze diverse data sources including electronic health records, neuroimaging, genetic information, and demographic data. Following thorough data preprocessing, model training, and evaluation using metrics like accuracy, precision, recall, F1 score, and Cohen's Kappa, the Random Forest algorithm emerges as the top performer with 98.7% accuracy. The research highlights the importance of addressing ethical considerations, such as protecting patient privacy, ensuring data security, and mitigating algorithmic bias when implementing AI in mental health services. The findings indicate that AI-based approaches can markedly improve diagnostic accuracy, provide tailored treatment suggestions, and facilitate early relapse detection, thus promoting proactive mental health management. Key Words: Artificial Intelligence, Mental Health, Data- Driven approach, Personalized Treatment, Machine Learning, Natural Language Processing.

Human Cyber Rights in the Context of Technological Singularity

Introduction: the physical and technological transformations of the modern world are accelerating due to the theory of technological singularity. This phenomenon indicates that the time between fundamental technological discoveries is shortening. In the context of such a dynamic development of technology, the existing law does not always have time to adapt to new challenges, which pushes for the need to revise legal norms and introduce new concepts. The purpose of the work is to explore fundamental issues related to the impact of technological singularity on human rights as well as to propose a new concept – human cyber rights. Methods: the methods of theoretical analysis and comparative jurisprudence were used in the course of the study. The analysis of literary sources on the research topic was carried out, including the works of scientists and jurists dealing with philosophy of law, technology, and human rights. The methods of logical analysis and synthesis were also used to identify the connections between fundamental concepts and develop a new concept. As a result of the study, the main problems associated with the lag of law from technological progress have been identified. The concept of human cyber rights has been proposed, which includes the rights of an individual in the digital space. The need to adapt legal norms to new challenges, such as the rights of scanned persons and ensuring data security in the digital space, has been discussed. The study has confirmed the need to revise the legal norms in the light of technological singularity and proposed a new concept of human cyber rights as a way to adapt law to the challenges of digital transformation. Further research in this area can contribute to the development of more flexible and effective legal mechanisms to ensure the protection of human rights and freedoms in the era of the digital revolution.

Multi‐Objective Federated Averaging Algorithm

ABSTRACTThe recent global trend is the convergence of information and communications technology (ICT). By applying ICT in various fields such as the humanities, new types of products and services are created, and new values that help people's lives can be created. AI can be selected as a representative technology in such convergence ICT. However, applying AI technology to actual production requires ensuring data security. Federated learning (FL) can achieve secure sharing of data, where all parties participate in model training locally and upload it to the server for aggregation. The data never leaves the parties involved, thus solving the problems of data privacy and data silos. However, FL faces issues such as high communication cost, imbalanced performance distribution among participants, and low privacy protection. To achieve a balance between model accuracy, communication cost, fairness, and privacy, this paper proposes a multi‐objective optimization‐based FL algorithm (M‐FedAvg). The multi‐objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA‐III. The experimental results show that the algorithm proposed can effectively reduce the communication cost of FL and achieve privacy protection for participants without affecting the accuracy of the global model.

Legal Protection of Notaries in Document Validation through Technology-Based Systems: A Comparative Legal Review of Indonesia, the United States, the Netherlands, and Australia

This paper examines the legal framework and challenges notaries face in adopting technology-based systems for document validation in Indonesia, with a comparative analysis involving the United States, the Netherlands, and Australia. Indonesia's legal framework, particularly Law No. 2 of 2014 on the Office of Notary, has yet to fully accommodate electronic signatures and remote notarization. The requirement for in-person meetings for authenticating notarial acts creates a regulatory barrier to implementing cyber notary services. Furthermore, Indonesian notaries face limited access to essential data systems, such as Dukcapil (Civil Registration Directorate) and BPN (National Land Agency), complicating the verification of personal identity and property documents. In contrast, the United States has adopted e-notary systems, using blockchain technology to ensure data integrity and mitigate fraud risks. The Netherlands provides an integrated platform, allowing notaries to validate documents remotely by accessing national databases in real time. Meanwhile, Australia has implemented a Digital Identity Program to facilitate efficient online authentication of documents. These innovations demonstrate how interoperability, regulatory flexibility, and digital literacy can enhance notary services and legal certainty. This paper proposes regulatory reforms in Indonesia, including revising existing laws to support remote notarization and electronic signatures. Second, developing integrated data platforms for real-time access. Third, providing training programs for notaries to adopt new technologies and ensure data security. By aligning Indonesia’s legal framework with international best practices, cyber notary services can enhance efficiency, security, and legal protection for both notaries and the public, meeting the demands of the digital era.Comparison

Securing UAV Communications with Homomorphic Encryption and Key Regeneration: A Model Checking Approach

Unmanned Aerial Vehicles (UAVs) are at the centre of multiple applications across various fields, including delivery, disaster response, and surveillance. However, its use of wireless communications exposes them to security risks such as information distortion and interception. The paper presents a security model based on Homomorphic Encryption (HE) and Key Regeneration (KR) with the incorporation of Model Checking to enhance UAV communication security. Our system employs homomorphic encryption that resolves the data security problem by ensuring data security at the transmission stage, where information is processed at the receiver end without decryption, thus maintaining privacy and the data processing policy at the UAV end. Furthermore, the key regeneration protocol updates the encryption keys regularly, thus reducing the risk denial window and impact in case of key compromise. The security of the protocols is proven through model checking that establishes the system status operation through a systematic study, as per the provided security requirements. The simulation results indicate certain trade-offs in the system with performance degraded in encryption time and computations, unlike the existing system. The system enhancement outweighs the performance, as indicated by the minimized level of threat and integrity violation. Therefore, integrating HE and KR can secure UAV communication in the presence of a smart and capable adversary, making the entire system robust and dependable in the line of operation.

DSDOS Cloud: A Decentralized Secure Data Outsourcing System With Hybrid Encryption, Blockchain Smart Contract‐Based Access Control, and Hash Authentication Codes for Cloud Security

ABSTRACTWith the increasing trend of outsourcing data to cloud services, ensuring data security and privacy has become crucial. Typically, data are stored on cloud servers in encrypted form to mitigate risks. However, accessing the encrypted data requires an access key distributed by a third party. If this third party is untrustworthy, it poses a significant security threat to the system. To address this challenge, we propose a Decentralized Secure Data Outsourcing System (DSDOS) that uses blockchain technology to ensure data security and privacy. The DSDOS system comprises three modules: data security and privacy, access control and authorization, and data integrity and availability. The data security and privacy module uses a hybrid encryption scheme that combines Advanced Encryption Standard (AES), partially homomorphic encryption (PHE), and Diffie–Hellman (DH) to ensure secure data storage and access. The access control and authorization module uses a blockchain‐based smart contract system to manage access to the encrypted data. The data integrity and availability module uses hash‐based message authentication code (HMAC) to ensure that the data are not tampered with and is always available. We conducted a security and performance analysis of the DSDOS system and found that it outperforms previous schemes in terms of security and performance. The DSDOS system is a secure and privacy‐preserving data outsourcing system that can be used to mitigate the security risks associated with traditional cloud storage systems.

Advancements in Network Attached Storage (NAS) for Mobile Devices: Technological Developments and Performance Assessment

Network-attached storage (NAS) technology has emerged as a vital solution in the evolving landscape of data management and storage. With the exponential growth of data and the increasing demand for efficient, scalable storage solutions, NAS systems offer a centralized, cost-effective, and flexible alternative to traditional storage methods. This paper explores the application and advantages of NAS technology in various scenarios, from home and small business environments to large enterprises. It highlights the key strengths of NAS systems in optimizing data storage, ensuring data security, and facilitating cross-device sharing. The study delves into the architectural components of NAS, including file systems, RAID configurations, and network protocols, to demonstrate how these technologies enhance storage performance and reliability. Technical details on NAS system design, such as the integration of advanced file systems and user-level networking technologies, are discussed to showcase their impact on data transfer efficiency and overall system performance. Experimental results validate the effectiveness of NAS in different environments, illustrating its ability to provide high availability, data redundancy, and low-latency data access. The findings underscore NAS technology’s potential as a versatile and scalable storage solution, with recommendations for future research focused on developing new file systems, optimizing network protocols, and exploring edge computing integrations to further enhance NAS capabilities.

Data governance challenges on smart home involving multiple research sites

Abstract Background The development of Ambient Assisted Living (AAL) technology has great potential for ubiquitous health monitoring through collaboration between multiple research sites, but its use poses significant challenges to data governance. This study aims to investigate data governance challenges arising when smart home data flows across different research sites. Methods A four-analytic-dimension method was used to identify potential data-related issues that may occur when heterogeneous smart home data moves around multiple research sites. These dimensions include data domain, stakeholders, value, and governance goal. Results Data domain has difficulty in categorizing new data types generated by analytical models without a specified agreement, and determining the regulations for varying levels of anonymization and privacy protection is unclear in the AAL research field. Stakeholders in the AAL project may hold have different norms and practices for data use. Although some common values may be shared, such as improving the quality of homecare, conflicts may still arise and need to be regulated by data governance. Shifting AAL data between stakeholders may reduce its value, as different stakeholders may have varying interests. For instance, removing identifiable information from data to preserve privacy may reduce its value for researchers exploring individuals’ activity patterns. Governance goals are objectives for maintaining and facilitating data. While some governance goals can be widely acknowledged and shared, such as ensuring data security and privacy, conflicts may still exist over others, such as data access. Conclusions The big data generated by smart home technology highlights the need to develop proper data governance models to regulate data. This study’s implications include a better understanding of potential data-related challenges when data flows around multiple research sites, helping policymakers set up suitable data governance models. Key messages • AAL tech for health monitoring poses data governance challenges due to varying stakeholder norms and values. • Proper data governance models need to be developed for AAL data as it remains challenges in data domain, stakeholders, value, and governance goal.

TrustCNAV: Certificateless aggregate authentication of civil navigation messages in GNSS

The Global Navigation Satellite System (GNSS) is capable of accurate positioning because it can provide high-precision data. These data are transmitted to the receiver in the form of navigation messages, called civil navigation messages (CNAV). As it is transmitted in an open, transparent environment without data integrity protection mechanisms and secure data transmission measures, the CNAV is suspected to spoofing attacks. In 2023, the OPSGROUP has received approximately 50 reports of GPS spoofing activity. A spoofed plane's navigation system will show it as being in a different place - a security risk if a jet is guided to fly into a hostile country's airspace. To prevent the forging of GNSS positioning data by spoofing attacks targeting CNAV, we propose a certificateless aggregation authentication for CNAV by using the elliptic curve discrete logarithm problem and the combination of the GNAV structural characteristics, called TrustCNAV. Security proof and performance analysis indicate that this authentication scheme can resist spoofing attacks and ensure data security of CNAV, also it avoids pairing operations with high computational complexity, thus meeting security requirements without causing too much time and communication consumption.

Automatic Indoor Thermal Comfort Monitoring Based on BIM and IoT Technology

Building Information Modeling (BIM) and Internet of Thing (IoT) integration technologies can improve operational efficiency in the operational phase of construction projects. Currently, research on the integration of BIM and IoT has yet to ensure secure data transmission and lacks real-time data processing capabilities. This study builds a framework to collect and analyze BIM and IoT data in real time. The framework is verified to be effective through a case study in an office building. The monitoring system can automatically calculate the Predicted Mean Vote (PMV) value, upload and update real-time temperature and humidity data, and visualize thermal comfort through heat maps. The proposed integration approach offers building management strategies to enhance thermal comfort in office environments, fostering a more inclusive and accommodating workspace that acknowledges the diverse cultural backgrounds of occupants.

A market-based load regulation method for heterogeneous residential air-conditioning loads under cloud-edge collaboration

Residential air-conditioning load on the power demand side has gradually increased. The air-conditioning loads have thermal storage characteristics, and adjusting their temperature settings will not cause significant negative impacts to users, making them well-suited for demand response. In existing studies, load aggregators usually serve as demand response implementors, and reducing the energy consumption levels of air conditioning loads directly. However, this method often lacks a clear definition of the flexibility of individual air-conditioning units and fails to account for their variations. The absence of well-defined commodity characteristics for the flexible regulation potential complicates pricing, making it difficult to safeguard the interests of the load aggregator and each air-conditioning user. Furthermore, the diversity among residential air-conditioning loads introduces high-dimensional variables into demand response optimization, making it challenging to balance data security with computational efficiency. To address these challenges, this paper proposes a market-based load reduction method for heterogeneous residential air-conditioning loads using cloud-edge collaboration. First, the flexible adjustment capability of air-conditioning loads is defined as a tradable commodity. Then, a cloud-edge collaborative trading scheme is proposed to guarantee the benefits of the load aggregator and each air-conditioning user in a data-neutrality manner. Finally, a hybrid method based on encrypted clustering is developed to compute the optimal trading strategy, ensuring data security and computational efficiency. Simulation results based on data from the Austin area show that Pareto improvement is achieved compared to other load reduction schemes, and the proposed encrypted clustering method reduces computational complexity and data security risks compared to traditional analytical and iterative approaches.

Progress and Applications of Fully Homomorphic Encryption

In the era of digital transformation, marked by the widespread adoption of cloud-based technologies such as cloud computing and storage, ensuring data security and protecting user privacy have emerged as critical industry concerns. Fully Homomorphic Encryption (FHE) addresses these issues by allowing various computations to be performed on ciphertexts without the need for decryption, thus safeguarding data security and privacy. This paper explores the mathematical and algorithmic foundations essential for understanding FHE. It provides a detailed analysis of the advancements in FHE schemes over the past decade, focusing on various mathematical problems, including ideal lattices and integers. The discussion extends to three practical applications: cloud computing, machine learning, and electronic voting, highlighting the progress and exploring potential future research avenues and developmental strategies in the field of FHE. This comprehensive review not only underscores the significance of FHE in enhancing data security but also charts a path for its future exploration and integration into emerging technologies.