A Secure and Efficient KA-PRE Scheme for Data Transmission in Remote Data Management Environments

The healthcare industry faces significant challenges in safeguarding data security and integrity, particularly with the rise of digital health systems and the sensitive nature of patient information. Blockchain technology offers a decentralized, immutable, and secure solution to address these issues, allowing healthcare organizations to protect and manage patient data effectively. This paper examines how blockchain can enhance data security and integrity within healthcare by examining its key features, such as immutability, transparency, and decentralized access, which collectively provide a robust framework for secure data management. The potential applications of blockchain in healthcare, including patient consent management, access control through smart contracts, and data-sharing solutions, are analyzed alongside challenges such as regulatory compliance, cost, and interoperability. By integrating blockchain, healthcare systems can achieve improved data security, fostering trust and resilience in patient data management. This paper concludes with case studies and discusses future trends in blockchain implementation within healthcare, underscoring its transformative potential for securing data while addressing privacy and ethical concerns. Keywords: Blockchain, healthcare, data security, data integrity, patient data management, smart contracts.

To study and solve the issues of big data security and privacy protection is the key to promote the healthy development of big data. In this paper, a 3D big data security and privacy protection model based on life cycle, security goal and user role is presented, and the security challenges and security goals of big data are analyzed from different aspects. The feasible technology and its application in big data security and privacy protection are expounded and analyzed, and the in-depth research direction of big data security and privacy protection is discussed and summarized.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

Personal big data pricing method based on differential privacy

With the rapid digitalization of educational institutions and the widespread adoption of training management platforms, ensuring robust data security and privacy protection has become a critical concern for educational stakeholders worldwide. This study presents a comprehensive testing design framework specifically tailored for data security and privacy protection in training management platforms. The research systematically examines the unique challenges posed by educational data environments, including sensitive student information handling, multi-stakeholder access controls, and regulatory compliance requirements under contemporary data protection legislation. Through the development of an integrated testing methodology that combines automated security scanning, privacy impact assessment procedures, and compliance verification protocols, this investigation demonstrates significant improvements in identifying and mitigating privacy vulnerabilities within educational platform environments. The proposed framework achieves 94% coverage of critical security scenarios while reducing privacy risk exposure by 67%, providing educational institutions with practical tools for maintaining data protection standards and ensuring regulatory compliance in increasingly complex digital learning environments.

Mobile edge computing (MEC) technology is widely used for real‐time and bandwidth‐intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real‐time data processing, and the consensus mechanism is not suitable for resource‐constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy‐preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof‐of‐Work (PoW) consensus mechanism are proposed to accommodate resource‐constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute‐based encryption (CP‐ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine‐grained control of access rights, and has higher throughput and security than traditional blockchain.

With the global spread of the COVID-19 epidemic, the traditional tourism industry has suffered an unprecedented impact, pushing the tourism industry towards digital transformation. As an important direction of this transformation, digital cultural tourism (digital Literature & Tourism) utilizes the latest digital technologies such as 3D simulation, Virtual Reality (VR), Augmented Reality (AR), etc., to provide a brand-new form of cultural and tourism experience. Taking the concentric lock platform as an example, this study explores the development and application under the innovation of digital cultural and tourism products, analyzes its contribution to the recovery of tourism, cultural inheritance and innovation, and puts forward the outlook for future development. Designed with the concepts of emotional interaction, cultural inheritance and technological innovation, the concentric lock platform provides a space where users can padlock their locks in a virtual environment to express their emotions and share their stories. The platform not only redefines the emotional connection between couples, but also provides a new profit model and cultural inheritance for tourist attractions. In terms of technical realization, the platform adopts cloud computing to support high concurrent access, and utilizes technologies such as WebGL and Three.js to render complex 3D graphics in web browsers to create realistic virtual attraction models for users, while focusing on data security and privacy protection. The platform's functional modules include the padlocking and viewing of virtual concentric locks, the withdrawal process of breaking up locks, as well as a rich cloud tourism experience and cultural heritage content, which meets the diversified needs of users. By cooperating with tourist attractions, the concentric lock platform developed the virtual concentric lock function with attractions as the background, promoting cultural heritage and tourism recovery. Especially during the epidemic, the platform provided global users with the possibility of cloud tourism and virtual emotional interaction, effectively mitigating the impact of the tourism industry affected by the epidemic. In the Discussion and Outlook section, this study points out that the concentric lock platform and similar digital cultural and tourism products face challenges and opportunities in terms of technological development, market demand, cultural heritage and social impact. In the future, with the application of new technologies such as 5G and artificial intelligence, more personalized and interactive cultural tourism experiences are expected to be developed. At the same time, how to effectively integrate online experiences with on-the-ground tourism activities, protect user privacy and ensure data security will be the key issues to be solved for digital cultural tourism products such as the concentric lock platform. To summarize, the concentric lock platform, as a typical case of digital cultural tourism, not only shows the potential of digital technology application in the field of cultural tourism, but also provides an effective strategy and practice case for the digital transformation of the tourism industry. With the continuous progress of technology and increasing consumer demand, the future development of the digital cultural tourism field will be broader, providing people with richer and deeper cultural tourism experience.

With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain.

Data security and privacy protection are increasingly becoming a bottleneck in the development of cloud computing. To improve the existing homomorphic encryption technology, ECC homomorphic encryption algorithm is proposed, and ECC homomorphic encryption algorithm is designed and implemented. Based on this, the aggregation model of data privacy protection is constructed, and the effect of ECC homomorphic encryption data security operation is analyzed. Compared with the traditional RSA encryption algorithm, ECC homomorphic encryption algorithm shows a higher level of security. When the key length increases gradually, the security of ECC with more than 300 bits is much greater than that of RSA with more than 2000 bits. At the same time, ECC homomorphic encryption algorithm is better than RSA in terms of encryption time and cipher size. ECC homomorphic encryption algorithm has the advantages of low computational complexity and good security performance.

Research in Public Administration and Public Management has distinctive features that influence the choices and application of research methods. The standard methodologies for researching from the social sciences can be difficult to follow in the complex world of the public sector. In a dynamic political context, the focus lies on solving societal problems whilst also using methodological principles to do scientifically sound research. The second edition of Research Methods in Public Administration and Public Management represents a comprehensive guide to undertaking and using research in Public Management and Administration. It is succinct but covers a wide variety of research strategies, including action research, experiments, case studies, desk research, systematic literature reviews and more. It pays attention to issues of design, sampling, research ethics and data management. This textbook does explain the role of theory, but also offers many international examples and practical exercises. It takes the reader through the journey of research, starting with the problem definition, choice of theory, research design options and tools to achieve impactful research. New and revised material includes, but is not limited to: A closer look at popular methods like the experiment and the systematic literature review; A deeper examination of research ethics and data management; New examples from a wide range of countries; Updated ‘Further Reading’ material and additional useful websites. This exciting new edition will be core reading for students at all levels as well as practitioners who are carrying out research on Public Management and Administration.

Relevance. In the modern world, artificial intelligence (AI) is one of the fastest growing areas of information technology, which is increasingly penetrating into various spheres of human activity. In the context of public and municipal management, AI is a technology that is able to automate and optimize work processes, improve the quality of services provided to citizens, and increase the level of security and transparency of government work.The purpose is to analyze the possibilities and risks of using artificial intelligence in state and municipal management, as well as to determine the prospects for its development in this area.Objectives: to analyze the advantages of using artificial intelligence in state and municipal management; to study the risks and challenges associated with its implementation; to analyze trends in the development of artificial intelligence in this area, as well as to determine the prospects for its use.Methodology. Empirical, theoretical, statistical and graphical representation methods were used in the course of scientific research.Results. The study revealed the advantages of AI in public administration (improving efficiency, improving the quality of services, transparency, security); risks and challenges were studied (lack of infrastructure, risk of privacy violations, problems of transparency of algorithms, risk of discrimination); trends in the development of AI were analyzed (e-government, optimization of workflows, personalization of services, monitoring of social media, security); the prospects of using AI (smart cities, virtual assistants, hybrid systems, decision support, cooperation with business, international cooperation) have been identified.Conclusions. Artificial intelligence can significantly improve the efficiency and quality of public and municipal administration. However, for the successful implementation of AI, it is necessary to solve problems such as the lack of infrastructure and qualified specialists, ensuring data security, transparency of algorithms and compliance with the regulatory framework. To do this, it is necessary to conduct further research, develop integrated approaches and take into account world experience. The prospects for using AI include the creation of smart cities, the use of virtual assistants, hybrid systems, decision support systems, business cooperation and international cooperation.

This study aims to explore how big data and the Internet of Things can improve the economic benefits of enterprises in online marketing. We first introduced the concept and development trend of big data, Internet of Things, and Internet marketing and then collected a large number of empirical data through the design and implementation of questionnaire survey. Our data analysis results show that the level of use of big data and the Internet of Things has a significant positive impact on business sales. However, there are also some problems and challenges in the implementation of big data and the Internet of Things, such as resource and technology limitations, and legal and ethical issues such as data security and privacy protection. Therefore, we suggest that enterprises should reasonably plan the use of big data and the Internet of Things according to their own resources and capabilities, while constantly upgrading their own technical level, focusing on data security and privacy protection, and tracking the latest technology and market trends. In addition, the government and industry organizations should also formulate and improve relevant policies and standards to support the development of big data and the Internet of Things and protect the interests of consumers. In general, big data and the Internet of Things provide new opportunities and challenges for enterprise online marketing, which need further research and discussion.

In view of the wide application and popularization of large data, more and more data security and privacy issues have brought great challenges to the development of large data. Starting from the characteristics of big data, this paper analyses various risks of information security, and puts forward the corresponding development strategy of big data security. The results show that the combination of technology and relevant policies and regulations can better solve the problem of big data security and privacy protection.

New generation of health care IT systems are collecting and storing more and more data of patients. Useful knowledge can be extracted from the data in EMR or PHR to provide medical advises to patients, while through data analysis the result statistics can be used to support the scientific research. However, RDBMSs-based framework is not able to support the requirements of massive health care data storage, management and analysis. To solve the problem, this paper proposes a massive data management and analysis solution based on Hadoop to archive better performance, scalability and fault tolerance. The data management framework is presented. Besides, 2 different data analysis methods based on MapReduce and Hive are proposed. Experiment results of data upload, data query and data analysis show that the performance of the proposed framework is greatly improved, and a brief summary of the performance and the differences between 2 methods of MapReduce and Hive is also discussed.