Ensure Data Security Research Articles

Role of Encryption/decryption Key in Cryptography

Abstract — Cryptography is a powerful technique for securing data and communication. cryptography is a technique used to encrypt and decrypt the data. A technique encryption (hiding information) data will be encoded which is used to provide secure data to prevent from fraud user and hackers where decryption is used to convert the encoded data into user readable format. It is used for storage and transmission compression is a reduction in the number of bits needed to represent the data."Cryptography algorithms serve the crucial purpose of safeguarding sensitive data. As the internet has seamlessly integrated into our daily lives, experiencing rapid growth over recent decades, ensuring data security has emerged as a paramount concern for every individual connected to the web. Data security guarantees that our information remains accessible solely to its intended recipient while thwarting any unauthorized alterations or tampering attempts. Achieving this high level of security necessitates the utilization of diverse algorithms and techniques. Cryptography encompasses methods that encode data through specific algorithms, rendering it indecipherable to the naked eye unless decoded using predetermined algorithms set by the sender.

A Dynamic Sandbox Detection Technique in a Private Cloud Environment

In specific private cloud scenarios, how to defend against malicious software and ensure data security is one of the current research hotspots, and sandbox is an important detection method. This paper proposes a dynamic behavior detection technique based on sandboxing, which real-time monitors and analyzes malicious software behavior. By improving the sandbox behavior weight, integrating virtual resources, and designing fine-grained access control, the detection accuracy and efficiency are enhanced based on zero trust access control system. The simulated attacks are identified on the testing platform, drawing knowledge graphs, achieving effective discovery and tracing. Meanwhile, this paper verified through experiments that the system consumption of the detection method is within an acceptable range, expanding the detection range and reducing the missed detection rate.

Mobile Device Security: A Two-Layered Approach with Blockchain and Sensor Technology for Theft Prevention

In the backdrop of the escalating incidents of mobile device theft and associated security challenges, a resilient and innovative solution is imperative. The traditional security mechanisms, largely reliant on the International Mobile Equipment Identity (IMEI), have been fraught with vulnerabilities, leading to a surge in incidents of device theft and data breaches. Addressing this pressing issue, we present a novel, two-tiered approach integrating sensor technology and blockchain to bolster mobile device security. This work aims to create and assess a dual-layered security strategy that uses blockchain and sensor technologies in a complementary way. Based on a rigorous conceptual framework, it explores a two-tiered security model that intricately combines sensor and blockchain technologies. This collaborative integration aims to provide an effective solution to the widespread challenges of theft and security breaches in mobile devices. The methodology employs a sensor layer for real-time data collection and processing to detect potential thefts, and activating alerts. The blockchain layer, invoked upon these alerts, initiates secure, transparent, and decentralized transactions for verification and validation across network nodes. This dual mechanism ensures swift and secure anti-theft actions, supported by an enhanced encryption standard. Our result analysis reveals the proposed system’s superiority in computational time, energy consumption, and overall security levels when compared to existing protocols. The integration of real-time processing and blockchain’s immutable nature promises reduced false positives and enhanced data integrity. The findings indicate that this integrative approach not only mitigates theft but also ensures data security, marking a significant stride in mobile security technology. In conclusion, this two-layered system promises a scalable, efficient, and robust solution to mobile device theft and data breaches, with potential impacts transcending individual device security to influence broader data privacy and security paradigms, thus signifying a pivotal development in the field of mobile security.

Enhancing Smart Healthcare Networks: Integrating Attribute-Based Encryption for Optimization and Anti-Corruption Mechanisms

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 milliseconds, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74%. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

CYBERSECURITY IN CLINICAL PRACTICE

Cybersecurity is a concern for patient safety. Recent cyberattacks on healthcare facilities around the world have exposed the risks to patients: from treatment delays due to hospital and clinic closures, to the threat harm from personal data theft, to patient death. The recent Covid-19 pandemic has further increased cyberattacks against healthcare organizations. Frontline healthcare workers are often warned about the dangers of poor data management and advised to take precautions to ensure data security. However, many workers are overwhelmed by conflicting administrative priorities, cybersecurity risks go unnoticed.

Improving Mathematics Education through Teacher Professional Development: A Tanzanian Perspective

This study aimed to explore mathematics teachers’ perceptions of professional development. The study involved two public secondary schools in Ilala Municipality, Dar es Salaam. This was a qualitative study based on case study design. The study involved fourteen (14) participants that were selected purposefully. Two (2) heads of the schools, two (2) Academic Teachers, two (2) heads of mathematics departments, and eight (8) mathematics teachers were equally involved from both schools. Interviews, focus group discussions, surveys, and document analysis methods were used in data collection. Both interviews and focus group discussion were audio-recorded by using a protected smartphone with password to ensure data security. The documents reviewed includes; school policies, school calendars, and mathematics departmental meetings’ minutes. The data collected were analysed thematically to identify and interpret key themes. The data analysis was based on the following steps: transcription, data reduction, presentation, and discussion of findings. Fictitious names were used for names of people and schools to ensure confidentiality and anonymity. The study revealed that teachers perceived professional development as formal programs to improve teachers’ quality that were time-consuming and costly, while informal professional development programs were overlooked by teachers and other educational stakeholders. The study recommends that, in order to improve teachers’ quality as intended in professional development, there is a need to raise awareness for both formal and informal professional development programs among teachers and a learning community as a whole.

The Economic Impact of AI Technology – Take Claude and GPT-4 As Examples

With the rapid development of science and technology, artificial intelligence (AI) technology has become the core field of global economic competition. Especially in recent years, the emergence of advanced AI models such as Claude and GPT-4 has not only led to a new round of technological revolution, but also had a profound and extensive impact on the global economy. The competition in these AI technologies is reshaping the economic pattern of various industries, creating new growth drivers, but also bringing many challenges and uncertainties. In the manufacturing sector, the application of AI technology has greatly improved production efficiency and quality, but it also requires companies to constantly update equipment and train employees to adapt to new production models. In the service industry, the application of AI customer service, intelligent recommendation and other technologies has improved the customer experience, but also put forward higher requirements for the skills and quality of employees. In addition, the wide application of AI technology also poses new challenges to data security, privacy protection and other aspects. How to enjoy the convenience brought by technology while ensuring data security and personal privacy has become an urgent problem to be solved. Therefore, in the face of the economic changes brought about by AI technology competition, policy makers, entrepreneurs and researchers need to conduct in-depth research and discussion, formulate reasonable policies and strategies, to better respond to challenges, seize opportunities, and promote the sustainable and healthy development of the economy.

Artificial intelligence scribe: A new era in medical documentation

The high workloads involved in clinical documentation represent one of the major factors contributing to the significant escalation of clinician burnout. The emergence of artificial intelligence (AI) has provided new avenues for relieving this burden by automating certain tasks like clinical documentation through the generation of clinical notes from a transcript of a clinical encounter. The advances in large language models (LLMs) have led to the emergence of such startups, but they come with their own set of challenges, predominantly surrounding the concerns of documentation accuracy, completeness, and data security. These can be addressed with a multi-faceted approach which could include fine-tuning the currently available models; using domain-specific models and in-house AI systems to ensure data security; and involving smaller LLMs and clinicians in the development and implementation of such systems. We can imagine a future where these systems are deeply incorporated into electronic health records, providing not only automated clinical documentation but also improving Clinical Decision Support systems, research, and patient communication.

A heterogeneous ring signcryption scheme with privacy protection and conditional tracing for smart grid

Smart grid develops rapidly, but there are still security risks such as user privacy leakage, power data tampering and audit data inconsistency. The existing schemes to ensure data security mainly use traceable ring signcryption, which is applied in distributed application scenarios such as smart grid. Traceable ring signcryption can ensure the anonymity, integrity, unforgeability and confidentiality of data, and can trace the real identity of anonymous users. However, the traceability of these schemes is arbitrary, any actor can trace the identity of anonymous users, and they do not resolve disputes caused by tampered or inconsistent data. To remedy these deficiencies, we combine ring signcryption with consortium blockchain technology for the first time to achieve privacy protection and conditional tracing, which can effectively avoid anonymous user identity being revealed at will. Consortium blockchain is a semi-distributed P2P network that can solve data disputes and is suitable for organizations that require certain access control mechanisms such as smart grid. In this paper, we propose a heterogeneous ring signcryption scheme with privacy protection and conditional tracing (CTHRSC) which between certificateless cryptographic system (CLC) and public key infrastructure (PKI). Besides, we prove that our scheme is secure under the discrete logarithm problem (DLP) and decisional Diffie–Hellman problem (DDHP) in random oracle model (ROM). Compared with other signature or signcryption schemes, our advantages are satisfying conditional tracing and known temporary session key security (KTSKS), requiring less computation cost and communication overhead.

Integrating IoT, Machine Learning, and Blockchain for Enhanced Network Security and Efficiency: A Review

The rapid expansion of the Internet of Things (IoT) has led to significant improvements in automation, communication, and data exchange across various industries. However, the integration of numerous interconnected devices has also heightened concerns regarding network security, data privacy, and system efficiency. This review explores the intersection of IoT, machine learning (ML), and blockchain technology as a holistic approach to enhancing network security and operational efficiency. IoT devices generate vast amounts of data, which, when combined with machine learning algorithms, can improve predictive analytics, anomaly detection, and real-time decision-making. Blockchain technology further strengthens this ecosystem by providing a decentralized and immutable ledger, ensuring secure data transmission and reducing vulnerabilities. The paper discusses recent advancements, key challenges, and future research directions, highlighting the potential of combining IoT, ML, and blockchain to create robust, secure, and efficient networks. Specific use cases, such as in healthcare, supply chain management, and smart cities, are also examined to illustrate practical implementations. This review concludes with insights into the future potential of these technologies and the challenges that remain in their integration.

Traffic and transportation management data storage terminal based on Internet of Things

Traditional data storage models are inadequate in the face of the growing demand for big data in transportation and transportation management. Its poor horizontal scalability makes it difficult to deal with the growth of massive data; on the other hand, its complex management makes it challenging to achieve unified management and effective resource utilization due to the differences in equipment from different manufacturers. In order to effectively store and manage this huge amount of information, it is urgent to rely on advanced technical tools. In this context, while ensuring data security and simplifying data management, it is also necessary to meet the terminal’s demand for high real-time performance, and these factors jointly promote the continuous attention and improvement of data storage terminal performance. This paper proposes a Hadoop solution based on distributed computing. As a distributed system infrastructure, Hadoop allows users to develop distributed programs without a deep understanding of distributed details, fully using the high-speed computing and storage capabilities of Hadoop clusters, which is especially suitable for big data processing tasks on the Internet of Things (IoT) platform. The experimental results show that for a 10 GB data file, the traditional terminal (Terminal 1) can store 7.8 GB, while the Hadoop-based terminal (Terminal 2) can store 9.9 GB. For 50 GB of data files, Terminal 1 and Terminal 2 can store 40.4 GB and 49.8 GB of data respectively. These results show that Hadoop terminals have significant advantages in processing large-scale data, especially in terms of data storage efficiency, and can use storage resources more effectively to meet the high-performance requirements of traffic and transportation management for data storage terminals.

Modifying supply chain strategies to address regulatory compliance challenges: Lessons from South Eastern Nigeria

Regulatory compliance was an essential component of contemporary corporate operations, guaranteeing conformity to laws, rules, and standards controlling different aspects of organisational activity. Businesses operating in South Eastern Nigeria encounter complex obstacles to regulatory compliance, such as currency depreciation, the dynamic nature of data protection legislation, and the increasing global integration of supply chains. This report offers valuable insights into the intricate process of adjusting supply chain strategies to address regulatory compliance difficulties in South Eastern Nigeria. The paper examined regulatory compliance difficulties and their effects on supply chain dynamics by conducting a thorough literature review and analysing current research. Devaluing currency led to changes in import/export levies, tariffs, and customs procedures, which need quick and adaptable actions to ensure compliance and competitiveness. In addition, the digitalization of corporate operations intensified the issues of ensuring data security and privacy compliance. This necessitated investing in technological solutions and adopting proactive risk management strategies. Despite these difficulties, firms in South Eastern Nigeria adopted a range of tactics, such as implementing compliance management systems, diversifying suppliers and markets, and engaging with regulatory agencies, to successfully modify their supply chain strategies. The results emphasised the significance of taking proactive actions, engaging in strategic planning, and fostering cooperation among relevant parties to effectively address regulatory compliance obstacles and maintain competitiveness in the global market. Keywords: Adherence to regulations, Techniques for managing supply chains, Depreciation of currency, Safeguarding data, Region of South Eastern Nigeria.

Regional Load Forecasting Scheme for Security Outsourcing Computation

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.

An Analysis of Managing the Cloud: Obstacles and Solutions for Efficient Administration and Protection

Cloud management offers possibilities and difficulties to enterprises looking for effective management and strong data security. This essay examines the challenges associated with cloud administration and suggests workarounds for these issues. Assuring data security, upholding legal compliance, cutting expenses, and overseeing intricate multi-cloud setups are important obstacles. The use of cloud-native monitoring and governance tools, the implementation of extensive data backup and recovery plans, the use of automation for resource provisioning and administration, and the implementation of strong security measures like encryption and access restrictions are all necessary for finding solutions. Organizations may improve the security of their important data assets and expedite their cloud administration procedures by tackling these challenges with suitable solutions. Ensuring data security is a major concern in cloud administration. Organizations are concerned about data breaches, illegal access, and industry regulatory compliance when they move critical data to the cloud. Organizations need to have strong security mechanisms like identity management, access restrictions, and encryption in place to lessen these risks. Furthermore, implementing a defense-in-depth strategy that incorporates several security tiers might improve cloud environments' overall resilience. Upholding legal requirements and industry norms is another challenge for cloud managers. To secure sensitive data and uphold client confidence, businesses in regulated sectors like healthcare and finance are subject to strict compliance regulations. Adherence to frameworks like HIPAA, GDPR, and PCI DSS, as well as thorough policies and frequent audits are necessary to achieve compliance in the cloud. Organizations may minimize the risk of non-compliance penalties and expedite compliance processes by putting governance tools and compliance automation systems into place. For enterprises using cloud resources, cost optimization is another major concern. Pay-as-you-go cloud services might result in overspending and financial restrictions due to improper resource utilization.

A low-power consumption security data fusion model for Industrial Internet of Things

Industrial Internet of Things (IIOT) is an important part of manufacturing industry in the future, and the front-end sensor data acquisition network is the core component of it. Due to the limited energy of IIOT, reducing transmission interaction, energy consumption, and computational complexity in the process of data transmission is the key and difficult point in designing model of IIOT. In this paper, a lightweight green security data fusion model for IIOT is proposed, called LCSDF model, which aims to achieve data security and integrity verifiability by using less energy consumption. The simulation results show that the proposed model can ensure data security and verifiability, and meet the energy consumption requirements of the system. The performance of the algorithm is also satisfactory, reflecting the lightweight, green, and security of the model.

A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system

With the widespread use of mobile communication and smart devices in the medical field, mobile healthcare has gained significant attention due to its ability to overcome geographical limitations and provide more efficient and high-quality medical services. In mobile healthcare, various instruments and wearable device data are collected, encrypted, and uploaded to the cloud, accessible to medical professionals, researchers, and insurance companies, among others. However, ensuring the security and privacy of healthcare data in the context of mobile networks has been a highly challenging issue. Certificateless signature schemes allow patients to conceal their respective privacy information for different sharing needs. Nevertheless, existing mobile healthcare data protection solutions suffer from costly certificate management and the inability to restrict signature verifiers. This paper proposes a certificateless designated verifier sanitizable signature for mobile healthcare scenarios, aiming to enhance the security and privacy of mobile healthcare data. This scheme enables the sanitization of sensitive data without the need for certificate management and allows for the specification of signature verifiers. This ensures the confidentiality of medical data, protects patient privacy, and prevents unauthorized access to healthcare data. Through security analysis and experimental comparisons, it is demonstrated that the proposed scheme is both efficient and effectively ensures data security and user privacy. Therefore, it is well-suited for privacy protection in mobile healthcare data.

Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants

BackgroundThe variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.ResultsTo address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.ConclusionsIn summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC.

Research on Grid Multi-Source Survey Data Sharing Algorithm for Cross-Professional and Cross-Departmental Operations Collaboration

This paper addresses the problem of multi-source survey data sharing in power system engineering by proposing two improved methods: a survey data sharing method combined with differential privacy and a permission change method based on attribute encryption. The survey data sharing method integrated with differential privacy achieves effective cross-professional and cross-departmental data sharing while ensuring data security by introducing multi-discriminator architecture and dynamic noise adjustment. To reduce the computational and communication overhead when user permissions change during survey data sharing, the attribute encryption-based permission change method supports dynamic changes in user permissions. The effectiveness of the proposed methods has been validated through targeted experiments in different scenarios. The work in this thesis provides a new solution for dynamic sharing of survey data in power network engineering. It contributes to the digital transformation of power network projects.

Artificial Intelligence and Ethics in Healthcare: A Bibliometric Analysis

The rapid proliferation of artificial intelligence (AI) in healthcare services has underscored the importance of ethical considerations. This development highlights the need to examine ethical implications, debates, concerns, and thoughts from diverse and broad perspectives. In this context, the study focuses on the ethical dimensions of AI in the healthcare domain. AI is increasingly being used in various healthcare applications, but this usage brings along ethical challenges. The aim of the research is to identify themes, trends, and critical points related to AI ethics in healthcare. Through literature review and bibliometric analyses, it is observed that AI ethics research in healthcare revolves around fundamental concepts such as ethics, AI, machine learning, healthcare services, and privacy. Additionally, the leading countries, authors, and institutions in the field are examined. The intensity of collaboration and knowledge sharing in the literature is steadily increasing. In conclusion, considering the potential benefits and challenges of AI use in healthcare, addressing ethical issues, ensuring data security, and enhancing transparency in AI decision processes are crucial. The study aims to provide a deeper understanding of AI ethics topics in the existing literature and guide future research.

Blockchain-Based Vendor Management in IT: Challenges and Solutions

The rapid evolution of the IT sector has led to increasingly complex vendor management systems, necessitating innovative solutions to handle the multifaceted challenges associated with these systems. Traditional vendor management practices often struggle with issues related to transparency, security, inefficiencies in communication, and trustworthiness among vendors. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a compelling solution to these challenges. This research paper explores the application of blockchain technology in IT vendor management, focusing on its potential to address critical challenges and enhance the overall efficiency of vendor-related processes. The paper begins by outlining the inherent challenges in conventional vendor management systems, including difficulties in verifying vendor credentials, managing contracts, ensuring data security, and maintaining a reliable audit trail. These challenges often result in operational inefficiencies, increased costs, and potential risks related to vendor fraud or non-compliance. Blockchain technology, known for its secure, transparent, and decentralized nature, offers a transformative approach to these challenges. By providing a distributed ledger that records all transactions in a secure and immutable manner, blockchain can significantly enhance the transparency and security of vendor management processes. The paper examines how blockchain can be used to automate vendor verification, streamline contract management through smart contracts, and ensure data integrity across the vendor lifecycle.