Data Security Technology Research Articles

Mechanism Study on Innovative Influences of AI and Blockchain on Supply Chain Logistics: Case Study of JD and Alibaba

This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.

Analysis of Key Technologies for Data Security Protection Based on Cloud Computing

With the rapid development of cloud computing, more and more enterprises and institutions are migrating their data and business operations to the cloud. However, this shift has brought data security issues in cloud environments to the forefront. This paper primarily investigates the key technologies for data security protection in cloud computing environments. It begins by introducing the basic concepts and architecture of cloud computing and analyzing the fundamental concepts and threats related to data security protection. The paper then delves into critical technologies such as data encryption, access control, data storage and backup security, and data privacy protection, detailing the algorithmic principles and implementation methods of these technologies. Additionally, the paper explores the challenges faced by current cloud computing data security protection technologies and their corresponding solutions, analyzing the effectiveness of these technologies through practical case studies. Finally, the paper forecasts the future development trends of cloud computing data security technologies, particularly the potential impact of emerging technologies on data security protection. This research aims to provide theoretical and technical support for data security protection in cloud computing environments.

Dynamic configuration and data security for bioinformatics cloud services with the Laniakea Dashboard.

Technological advances in high-throughput technologies improve our ability to explore the molecular mechanisms of life. Computational infrastructures for scientific applications fulfil a critical role in harnessing this potential. However, there is an ongoing need to improve accessibility and implement robust data security technologies to allow the processing of sensitive data, particularly human genetic data. Scientific clouds have emerged as a promising solution to meet these needs. We present three components of the Laniakea software stack, initially developed to support the provision of private on-demand Galaxy instances. These components can be adopted by providers of scientific cloud services built on the INDIGO PaaS layer. The Dashboard translates configuration template files into user-friendly web interfaces, enabling the easy configuration and launch of on-demand applications. The secret management and the encryption components, integrated within the Dashboard, support the secure handling of passphrases and credentials and the deployment of block-level encrypted storage volumes for managing sensitive data in the cloud environment. By adopting these software components, scientific cloud providers can develop convenient, secureand efficient on-demand services for their users.

Data acquisition and application of internet of things in financial management

With the rapid development of information technology, Internet of Things technology has become a hot topic in today’s society. This study aims to explore how IoT technology impacts various aspects of financial management, in particular how it changes the collection, processing, and parsing of financial data. Through in-depth literature review, questionnaire survey and experimental design, the research found that IoT technology has a significant positive effect on sales, transaction times and inventory control. But at the same time, data security, privacy concerns and initial technology investment are also challenges that enterprises need to consider when adopting these technologies. Overall, IoT technology offers tremendous opportunities for financial management, but businesses must fully assess the potential risks and benefits when implementing it.

Application of Internet of Things Technology in Computer Network Security and Remote-Control Analysis

Internet of things computer network technology can not only speed up the information transmission, but also help to break the previous time and space restrictions and improve the actual work efficiency. However, in the practical application and development of this technology, the realization of data security technology and remote control technology is the key to improve its application effect and ensure data security. Due to the obvious openness of the internet of things computer network itself, common security risks emerge one after another. Therefore, in practical application, it is necessary to pay more attention to security management and maintain network security.

Implementation of Consumer Personal Data Protection in Ecommerce from the Perspective of Law No. 27 of 2022

This research aims to analyze the implementation of the Personal Data Protection Law (PDP Law) No. 27 of 2022 in the e-commerce sector in Indonesia, with a focus on consumer data protection. The method used is normative juridical, focusing on analyzing relevant documents and regulations to outline the legal framework and its application in e-commerce practices. Sources of legal materials include laws and regulations, academic literature, and interviews with legal experts and e-commerce practitioners. The results show that e-commerce companies have made significant adjustments to comply with the PDP Law, including improved privacy policies and investments in data security technologies. However, challenges to the full implementation of the PDP Law remain, particularly regarding consumers' and companies' awareness and understanding of the rights and obligations stipulated in the PDP Law. While there has been an increase in consumer trust in e-commerce platforms, increasing consumer awareness and active participation in the protection of their data still requires further efforts. The implication of this study is that the PDP Law No. 27 of 2022 has provided a stronger and more comprehensive legal framework for consumer data protection in Indonesia's e-commerce sector. However, to maximize the effectiveness of this law, a concerted effort is needed between regulators, the e-commerce industry, and the wider public to improve education and awareness of personal data protection, as well as ongoing adjustments to data management practices by e-commerce companies.

Enhancing healthcare services through cloud service: a systematic review

Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare.

Research on the Challenges and Countermeasures of Enterprise Financial Risk Prevention under Big Data Environment Research on Data Security and Processing Technology

Research on the Challenges and Countermeasures of Enterprise Financial Risk Prevention under Big Data Environment Research on Data Security and Processing Technology

Exploration of Energy Meter Data Security and Fast Response Technology in Spot Power Trading

Abstract With the advancement of smart grid construction, higher requirements have been put forward for energy meter data, which need to realize real-time, accurate, efficient, safe, and economical data transmission. In this paper, an intelligent collection of energy meter data is designed to monitor energy meter data. The PCA algorithm is utilized to downscale the energy meter data, and the LMR algorithm is combined to monitor abnormal data from the meter. Based on the smart contract in blockchain technology, a parallel Pedersen commitment algorithm based on privacy protection is designed, a hybrid signature algorithm is created to ensure the secure transmission of energy meter data and a cluster load balancing model for energy meter data is also designed in combination with Hopfield neural network. The PCA-LMR algorithm identifies 95 anomalies of the data in 12 months and under the encryption of a smart contract. The packet loss rate of energy meter data is maintained at about 2%, and after optimizing the cluster load capacity of energy meter data using the Hopfield neural network, the cluster load variance value is about 1.84 in 20 h. Modern technology can improve the secure transmission of energy meter data and enhance its rapid response ability to ensure the economic benefits of the power system.

Video surveillance legislation in the EU and Slovenia: privacy rights, security needs, and data protection challenges

This paper describes the legal framework of video surveillance in the European Union (EU), with an emphasis on the legislation of the Republic of Slovenia. We are analyzing the legal intricacies inherent in balancing privacy rights and security needs. The purpose of the paper is to examine the legislative instruments in the EU and Slovenia, especially regarding the legal basis for processing data of video surveillance. The research methodology combines qualitative analysis and legislative review and case studies from selected Slovenian Information Commissioner opinions and decisions. The findings reveal a dynamic and complex landscape, where technology often outpaces regulatory frameworks, leading to potential privacy infringements. The paper shares the idea that harmonizing legislation across the EU helps ensure the balance between security and privacy rights, with significant practical implications in the creation of standardized policies and guidelines for the operation of video surveillance systems regarding European Data Protection Board opinions. The originality of this paper lies in its selected analysis of EU legislation and its practical application in Slovenian legislation. It distinguishes itself by offering a practical view in ensuring data protection compliance and security technologies regarding video surveillance.

Research on Vulnerabilities in Computer Information Technology and Encryption Techniques

In this era of information, the rapid advancement of computer information technology has brought about tremendous changes to our work and life. However, accompanying this progress are significant challenges in terms of data security. Vulnerabilities in data security and encryption technologies have become the focal point of research, aiming to ensure the utmost protection and security of our data. The purpose of this article is to delve into the vulnerabilities in data security and encryption technologies within the realm of computer information technology, with the intention of providing readers with a profound understanding of data security issues and effective solutions. By comprehensively understanding the origins and impacts of data security vulnerabilities, as well as the principles and applications of encryption technologies, we can become more acutely aware of the importance of data security, enhance our capabilities in safeguarding data security, and contribute to the construction of a secure and reliable information society.

A solution to smart health and state of art

A medical cyber–physical system (MCPS) is a unique cyber–physical system (CPS), which combines embedded software control devices, networking capabilities, and complex physiological dynamics of patients in the modern medical field. In the process of communication, device, and information system interaction of MCPS, medical cyber–physical data are generated digitally, stored electronically, and accessed remotely by medical staff or patients. With the advent of the era of medical big data, a large amount of medical cyber–physical data is collected, and its sharing provides great value for diagnosis, pathological analysis, epidemic tracking, pharmaceutical, insurance, and so on. This overview will present MCPS’s architectures and frameworks from different perspectives, modeling and verification methods, identification and sign sensing technologies, key communications’ technologies, data storage and analysis technologies, monitoring systems, data security and privacy protection technologies, and key research perspectives and directions. We can have a com- prehensive understanding of the important characteristics and technical route of MCPS, and grasp its research status and progress.

Privacy and integrity-preserving data aggregation scheme for wireless sensor networks digital twins

The security technology of digital twin is an important guarantee to ensure the security of digital twin operation, which mainly includes network security technology, data security technology and privacy protection technology. In wireless sensor networks, data aggregation technologies are known as a suitable solution to reduce energy consumption. In addition, due to wireless communications, wireless sensor networks are subject to many attacks. Therefore, it is very important to provide data security in the data aggregation process. In this paper, in order to protect data privacy and verify data integrity, moreover, balance the energy consumption and security during the data aggregation, we present a privacy and integrity–preserving data aggregation scheme for wireless sensor networks based on digital twins technology and homomorphic fingerprinting (HFPIDA). The HFPIDA adopts privacy function to protect data privacy and adopts homomorphic fingerprinting technology to verify the aggregation data integrity. Security analysis shows that the HFPIDA can effectively preserve data privacy and verify data integrity. Simulation results show that the HFPIDA requires less communication and energy overheads, and can achieve higher aggregation accuracy.

Social Engineering SWOT Analysis in Government-Owned Commercial Banks and National Private Commercial Banks

This research examines the phenomenon of social engineering at government-owned commercial banks and national private commercial banks. The research method used is descriptive qualitative with a literature study. The research results show the bank's strengths, weaknesses, opportunities, and threats. In addition, several strategies are recommended for banks to prevent social engineering attacks, namely building information technology in banking according to the standards and regulations of the Financial Service Authority (Otoritas Jasa Keuangan), utilizing social media as an educational tool, training employees, monitoring and optimizing data security and banking information technology networks, suppressing the circulation of social issues on behalf of banks that can trigger social engineering, increasing financial literacy and awareness of data security personal customers and employees. To prevent social engineering attacks, banks can implement strategies that are considered adequate.

Application of Big Data Technology in Enterprise Information Security Management and Risk Assessment

Nowadays, the application of enterprise management information has been deeply rooted in daily business activities, and the management risk of enterprise information security (EIS) has also increased. The use of advanced means to provide security protection for it has become the top priority. To optimize the EIS management system, and carry on the risk assessment (RA), firstly, this study analyzes the current situation of the enterprise's internal information management and summarizes the shortcomings and security risks faced by the system. In the era of big data (BD), the security risks of database information systems show a diversified trend. Secondly, to explore the application of BD technology in EIS management, the characteristics of this technology and security control measures for risks are summarized to strengthen the enterprise's information management innovation and implement the application of data security technology.

Evolutionary Analysis of the Regulation of Data Abuse in Digital Platforms

This study proposes a tripartite evolutionary game model to investigate the interactions among digital platforms, governments, and users to address the negative consequences of data abuse. The paper identifies that the high tax incentives and low penalties set by the government will increase the incentive for data abuse by platforms of different sizes, and the government can try to set up a tax ladder policy for platforms of different sizes and a dynamic penalty amount based on platform revenue. The study also reveals that user participation in supervision can reduce information asymmetry, and decrease the cost of government regulation. However, the single constraint of users is less effective than government regulation or dual user-government regulation. Additionally, the presence of privacy leakage risks prompts digital platforms to adopt compound engines to implement data abuse. Hence, the relevant government regulatory policies should consider the efficiency and cost of data security technology for timely adjustments. This research contributes to understanding the complex relationships among digital platforms, governments, and users and highlights the need for appropriate measures to mitigate the negative effects of data abuse.

Research on The Application of Blockchain-Based Data Security in Healthcare

This study focuses on the use of blockchain data security technology and its application in pharmaceutical companies, providing a theoretical foundation for achieving digitalization, efficient traceability, and ensuring the quality and safety of drugs in the rapidly advancing technological landscape. The research is based on a thorough review of the literature, supplemented by case studies, and provides insights into the data security scheme required for pharmaceutical companies' traceability processes. It also highlights the various steps involved in the traceability procedures, which have the potential to produce data and involve multiple participants. Effective technical security measures should be employed to manage and use the data to ensure its security and provide tools for maintaining drug quality. Also, the study highlights the technical foundation required to implement data security management, including blockchain and data security technology in the various data states analyzed, provides valuable management insights for medical enterprises or institutions, and explores the realization principles of various data security technologies in conjunction with blockchain technology. The research provides a comprehensive application of literature and cases and serves as a guide for enterprises' actual data security management, providing resources for pharma companies undergoing digital transformation to make strategic decisions about information management. Overall, the study offers valuable insights for learning lessons and generating new ideas in pharmaceutical traceability.

CNS: Research on Data Security Technology and Network Data Security Regulations Driven by Digital Economy

In order to improve data security and network data security in the digital economy-driven environment, this paper combines data security technology and network security technology to build a digital economy security management and control system. Moreover, this paper describes the data encryption of the data owners before the framework index and the composition and construction method of the corresponding EncIR tree, and analyzes the spatial keyword group query algorithm on the EncIR tree. In addition, this paper analyzes the experimental performance of index building and spatial query, and builds an intelligent digital economy security system on the basis of these algorithms. The experimental research results verify that the data security technology and network data system driven by the digital economy have good security performance, and on this basis, follow-up security regulations can be formulated.

Transformer-Based Subject-Sensitive Hashing for Integrity Authentication of High-Resolution Remote Sensing (HRRS) Images

The implicit prerequisite for using HRRS images is that the images can be trusted. Otherwise, their value would be greatly reduced. As a new data security technology, subject-sensitive hashing overcomes the shortcomings of existing integrity authentication methods and could realize subject-sensitive authentication of HRRS images. However, shortcomings of the existing algorithm, in terms of robustness, limit its application. For example, the lack of robustness against JPEG compression makes existing algorithms more passive in some applications. To enhance the robustness, we proposed a Transformer-based subject-sensitive hashing algorithm. In this paper, first, we designed a Transformer-based HRRS image feature extraction network by improving Swin-Unet. Next, subject-sensitive features of HRRS images were extracted by this improved Swin-Unet. Then, the hash sequence was generated through a feature coding method that combined mapping mechanisms with principal component analysis (PCA). Our experimental results showed that the robustness of the proposed algorithm was greatly improved in comparison with existing algorithms, especially the robustness against JPEG compression.

Medical Cyber–Physical Systems: A Solution to Smart Health and the State of the Art

A medical cyber–physical system (MCPS) is a unique cyber–physical system (CPS), which combines embedded software control devices, networking capabilities, and complex physiological dynamics of patients in the modern medical field. In the process of communication, device, and information system interaction of MCPS, medical cyber–physical data are generated digitally, stored electronically, and accessed remotely by medical staff or patients. With the advent of the era of medical big data, a large amount of medical cyber–physical data is collected, and its sharing provides great value for diagnosis, pathological analysis, epidemic tracking, pharmaceutical, insurance, and so on. This overview will present MCPS’s architectures and frameworks from different perspectives, modeling and verification methods, identification and sign sensing technologies, key communications’ technologies, data storage and analysis technologies, monitoring systems, data security and privacy protection technologies, and key research perspectives and directions. We can have a comprehensive understanding of the important characteristics and technical route of MCPS, and grasp its research status and progress.