Blockchain and Machine Learning Approaches to Enhancing Data Privacy and Securing Distributed Systems

This study provides a comprehensive examination of data privacy and security within the realm of environmental research, highlighting the evolving challenges and innovative solutions in safeguarding sensitive environmental data. Employing a systematic literature review and content analysis, this research scrutinizes peer-reviewed articles, legal documents, and policy papers published from 2014 to 2024. The study's objectives were to explore fundamental principles, assess key developments and cutting-edge solutions, and understand the impact of international standards and regulatory bodies on data protection in environmental studies. Key findings reveal a growing emphasis on robust data governance frameworks, driven by advancements in digital technologies and the global diffusion of regulations such as the GDPR. Innovations in encryption and anonymization techniques have emerged as critical tools in enhancing data security. However, the study identifies a pressing need for interdisciplinary collaboration and stakeholder engagement to navigate the complex landscape of data privacy and security effectively. The study concludes that the future of environmental research is intricately linked to the strategic integration of data privacy and security measures. It recommends a proactive approach to data protection, emphasizing the importance of compliance with international regulations, the adoption of privacy-by-design principles, and the development of education and training programs. Future research should focus on advancing data security technologies and exploring the socio-ethical implications of data use in environmental studies, ensuring the ethical stewardship of sensitive information. Keywords: Data Privacy, Environmental Research, Data Security Solutions, International Regulations.

In the era of big data and machine learning, data privacy has become an increasingly critical concern. Traditional centralized data processing methods pose significant risks related to data leakage, unauthorized access, and regulatory non-compliance. Federated Learning (FL) and Distributed Databases (DD) have emerged as promising solutions to address these challenges. Federated Learning enables collaborative model training across decentralized devices or servers while keeping data localized, thus preserving privacy. Similarly, Distributed Databases facilitate secure data storage and access across multiple nodes, reducing the risks associated with single points of failure. This paper explores the synergy between Federated Learning and Distributed Databases in enhancing data privacy, providing a comprehensive review of the current literature, methodologies, and implementations. We examine key protocols, frameworks, and security mechanisms that support privacy-preserving machine learning. Through detailed analysis and case studies, we present the effectiveness of integrated FL-DD systems in real- world scenarios such as healthcare, finance, and IoT. We conclude with a discussion on challenges, future research directions, and the implications of combining FL and DD for building secure, scalable, and privacy-aware data systems. Keywords-Federated Learning, Distributed Databases, Data Privacy, Machine Learning, Secure Computation, Data Decentralization, Privacy- Preserving Models, IoT Security, Blockchain, Data Governance

The paper explores how blockchain technology can transform data management, especially when it comes to handling privacy and security issues. Blockchain's immutability, transparency, and decentralization make it a potentially useful tool for addressing the expanding problems associated with data security and integrity. However, the slow adoption of blockchain technology is influenced by a complex interplay of developing privacy concerns, legislative uncertainty, and technological subtleties. The current study aims to identify key data privacy, protection, and security challenges associated with blockchain adoption in various domains, assess the adequacy of the current data privacy, protection, and security laws to address these challenges, and propose potential solutions and areas for further research to mitigate data privacy, protection, and security risks in blockchain applications. Additionally, the research has examined the adaptability and dependability of the two main kinds of blockchain—public and private—as well as the variations in their reach and visibility.

Student health records play a pivotal role in nursing education, offering a comprehensive insight into students' health histories, immunization statuses, and clinical experiences. This study aims to identify the challenges and specific needs of NEUST-CON students to develop an efficient health record management system, design an Electronic Medical Record that outlines data collection, storage, security, and access protocols, and create a system prototype incorporating essential features and functionalities based on user requirements. This study utilizes applied quantitative research to gather numerical data, objectively measuring factors like the frequency and impact of issues related to record accessibility, availability, data retrieval and update, and data security and privacy. Furthermore, it employs a descriptive correlational design to examine the relationships between the demographic profile and the challenges on the student health information record system among NEUST-CON students. Findings revealed that most nursing students at NEUST are female, aged 18 to 21, primarily in their first year. The study identified challenges related to availability, accessibility, data retrieval and update, and data security and privacy in student health information records. Notably, availability emerged as the most pressing challenge. While overall satisfaction was reported among respondents, data security and privacy concerns persisted. A significant relationship was also observed between students' year levels and ages. Therefore, enhancements in infrastructure and functionalities are essential to ensure consistent availability of data and resources, mitigating downtime and ensuring uninterrupted access to the SHIR prototype system.

Digital age data security and privacy have become vital. This is especially true in the United States, where data breaches and illegal access of private data are rising together with cybersecurity issues. This article examines how blockchain technology improves data security and privacy by using its distributed, transparent ground coupled with a cryptographic security architecture. Unlike traditional centralized databases that are vulnerable to cyberattacks and system collapse, blockchain runs on a distributed ledger system, where everyone can share information about past transactions, but no individual has the entire data set. Although there have been numerous factors shaping the development of blockchain technology over time—from its predecessors and distributed systems to early Internet cryptography developments such as cryptographic hashing, consensus mechanisms, and smart contracts—blockchain’s security-enhancing features have worked wonders in different fields such as healthcare, finance, and government operations. In healthcare, blockchain gives protection and integrity to electronic medical records. This mechanism also allows for secure data sharing between authenticated parties. In the financial sector, trading fraud protection, as well as secure transactions digitally, are more popular than ever. Through blockchain-based systems, industry-specific identity verification is now more efficient and less costly. Additionally, government departments are increasingly looking at blockchain’s role in secure voting systems, protecting citizens’ personal information, and maintaining transparent records. This article also touches on the United States’ regulatory framework, discussing everything from the Health Insurance Portability and Accountability Act (HIPAA) to the California Consumer Privacy Act (CCPA) and General Data Protection Regulation (GDPR). It demonstrates how blockchain can be used in conjunction with such an environment to guarantee compliance and data protection. While blockchain holds promise, its particularly rapid spread faces several issues. These include high costs of initial implementation, lack of scalability, and regulatory uncertainty. Many factors resist the advancement being achieved or impede what would otherwise be unheard-of expansion in underlying technology innovation and corporate accomplishments. In the end, this article emphasizes how blockchain technology transforms data security and privacy and stresses its part in building a more resilient digital infrastructure more fit for the United States. Blockchain can become a pillar for protecting data in a world growing more linked once these obstacles are solved and the creativity let flourish.

Review on application progress of federated learning model and security hazard protection

As social media becomes more and more popular, fake news spreads rapidly which is more likely to cause serious consequences, especially during the COVID-19 pandemic. On the premise of meeting data privacy and security requirements, federated learning uses multi-party heterogeneous data to further promote machine learning. This paper proposes a federal learning based COVID-19 fake news detection model with deep self-attention network (FL_FNDM). We construct a deep self-attention network for fake news detection, which combines self-attention-based pretrained model BERT and deep convolutional neural network to detect fake news. Moreover, the fake news detection model is learned under the framework of horizontal federated learning, aiming at protecting users’ data security and privacy. The experimental results demonstrate that the proposed model can improve the performance of fake news detection on the COVID-19 dataset, which can achieve almost the same effect of sharing data without leaking user data.

Data Classification for Achieving Security in Cloud Computing

Aviation Internet of Things (AIoT) data sharing can create tremendous value for participants. With the development of AIoT and intelligent civil aviation, data security and privacy protection have become more prominent. To improve the data security and privacy of AIoT, and allow heterogeneous devices to participate in federated training, in this article, a blockchain-based federated knowledge distillation (BFKD) is proposed. First, federated knowledge distillation is used for knowledge transferring between clients to protect client data privacy. Second, a clustering algorithm is used to select aggregated soft labels to enhance Byzantine resistance. Finally, the consortium blockchain validates the data exchange process of federated knowledge distillation. In addition, a federated score grouping practical Byzantine fault-tolerant algorithm is proposed for improving consensus efficiency and to encourage participants to contribute more public data and honestly participate in federated training. Theoretical analysis and experimental results show that BFKD boasts high data mining performance and communication efficiency while safeguarding data privacy and security.

Cloud computing performs several functionalities, and one of the most important functionalities is the storage and processing of data or information. With day-by-day enhancement of technology, cloud has been overburdened, and to address this issue, the concept of fog computing has been introduced. Fog computing is an extension of the properties of cloud computing to the network’s edge and additionally overcomes its limitations. Despite the growing fame of fog services, assuring the security and privacy of data is still a big challenge. Distributed denial of service (DDoS) attack is a well-known threat among the security concerns and an important research challenge when talking particularly about security of data in fog computing environment. Therefore, this chapter presents a survey which encompasses the various concepts of fog computing, DDoS attacks and some DDoS mitigation techniques, thus providing a comprehensive review. In addition, it beholds the future work in this domain. This chapter will attract new researchers and also strengthen the concept of fog computing.

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Internet of Things (IoT) is a domain where the transfer of big data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality, and payload capacity in comparison with the existing state of art methods.

Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in ML, that can help in fulfilling the challenges faced by conventional ML approaches in IoUT. This article presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.

In order to protect the security and privacy of big data, the cloud storage service needs to enforce effective access control mechanism on user requests. Attribute-Based Encryption is a promising cryptographic access control technique to ensure the end-to-end security of data in cloud. However, the existing ABE researches mainly focus on the efficiency decryption, while the flexibility of policy, the communication cost, and the metadata management of ciphertexts are still challenging issues in the big data environment. In this paper, for the first time, we propose a new distributed, scalable and fine-grained access control scheme based on classification attributes for the cloud object storage. The classification attributes and threshold policies are integrated into an access structure, and then the objects are encrypted with the integrated access structure. The constant-size ciphertext components related to attributes can be managed as the corresponding metadata. As a result the encryption complexity and ciphertext storage are reduced. In addition, we present a new label-based access control model with multi-authorities to describe the detailed relationships of entities in our scheme. Besides, the proposed scheme is proved to be secure under l-BDHE assumption, and the system implementation demonstrates the practical feasibility and good performance.

Introduction: The implementation of digital solutions in primary health care has brought significant changes and improvements to traditional systems. However, the integration between traditional medical cultures and digital solutions remains a hurdle to be overcome. Despite this, the implementation of these solutions brings advantages such as improved access to care, increased patient engagement, enhanced efficiency, and better health outcomes. However, there are challenges to be overcome, such as resistance to change, concerns about privacy and data security, as well as infrastructure and internet access issues. Through collaboration between healthcare providers, policymakers, and technology developers, it is possible to successfully overcome these challenges and integrate digital solutions into primary health care. Methodology: An integrative literature review was conducted to analyze case studies and relevant research on the implementation of digital solutions in primary health care. Articles published between 2010 and 2023 were selected from databases such as PubMed, Scielo, and Medline, addressing the benefits, challenges, and outcomes related to digital solutions. Data analysis was performed qualitatively and quantitatively, grouping relevant information and highlighting improvements in primary health care. Objective: This scientific article aims to explore the impact of digital solutions in primary health care, comparing and contrasting traditional systems with the advantages and challenges of implementing digital solutions. Discussion: The implementation of digital solutions in primary health care has brought significant advantages, such as improved access to care, increased efficiency, and better health outcomes. However, it also presents challenges, including resistance to change, concerns about data privacy, and the need to assess cost-effectiveness. Case studies highlight both successful and unsuccessful implementations, providing valuable insights into the factors that contribute to success or failure. To harness the full potential of digital solutions in primary health care, it is crucial to consider the impact on the doctor-patient relationship, ensure effective data management and analysis, and integrate digital solutions into existing healthcare systems. Additionally, ethical considerations, adequate training for healthcare professionals, and patient acceptance play important roles in the successful implementation of these solutions. Conclusion: The implementation of digital solutions in primary health care brings various advantages, such as improved access, increased efficiency, and better health outcomes. However, it is essential to address ethical issues, such as data privacy and security, and ensure that these solutions are accepted, effective, and integrated into existing healthcare systems. Training and education for healthcare professionals play a crucial role in the successful adoption of these solutions, as well as patient acceptance and adoption. Looking to the future, advances in digital technology have great potential to transform primary health care, provided the necessary skills are considered and there is collaboration among stakeholders. By addressing these considerations, primary health care can make the most of the benefits of digital solutions, improving healthcare delivery for individuals.