Data Privacy and Security in AI

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.

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.

The Internet of Things (IoT) has shown rapid growth in recent years. However, it presents challenges related to the lack of standardization of communication produced by different types of devices. Another problem area is the security and privacy of data generated by IoT devices. Thus, with the focus on grouping, analyzing, and classifying existing data security and privacy methods in IoT, based on data anonymization, we have conducted a Systematic Literature Review (SLR). We have therefore reviewed the history of works developing solutions for security and privacy in the IoT, particularly data anonymization and the leading technologies used by researchers in their work. We also discussed the challenges and future directions for research. The objective of the work is to give order to the main approaches that promise to provide or facilitate data privacy using anonymization in the IoT area. The study’s results can help us understand the best anonymization techniques to provide data security and privacy in IoT environments. In addition, the findings can also help us understand the limitations of existing approaches and identify areas for improvement. The results found in most of the studies analyzed indicate a lack of consensus in the following areas: (i) with regard to a solution with a standardized methodology to be applied in all scenarios that encompass IoT; (ii) the use of different techniques to anonymize the data; and (iii), the resolution of privacy issues. On the other hand, results made available by the k-anonymity technique proved efficient in combination with other techniques. In this context, data privacy presents one of the main challenges for broadening secure domains in applying privacy with anonymity.

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.

Modern marketing tactics and corporate operations have been impacted by the development of data privacy laws, which have altered how businesses gather, store, and use customer data. Concerns regarding data security and privacy have been highlighted by businesses' growing dependence on customer data for strategic decision-making and targeted advertising. To ensure accountability, transparency, and the preservation of consumer rights, governments throughout the world have responded with strict laws like the California Consumer Privacy Act (CCPA) and the General Data Preservation Regulation (GDPR) of the European Union. These policies radically change data management techniques by imposing stringent compliance criteria including user permission, data minimization, and access rights. Although compliance gives companies the chance to build their brand and draw in new customers, it also comes with drawbacks including high operating expenses, intricate data processing, and certain legal risks. This study looks at how data privacy laws affect marketing tactics, highlighting the move towards privacy-first initiatives, anonymised data usage, and safe online interactions. To identify new trends, obstacles, and best practices in privacy-compliant marketing, the study qualitatively analyses secondary sources, such as scholarly publications, official documents, and case studies. The results show how crucial it is for companies to include data privacy into their strategic planning to balance competitive advantage with regulatory compliance. Organizations must proactively adjust as global privacy standards continue to change in order to stay in compliance and cultivate client relationships in a market where privacy concerns are on the rise.

Deep learning has become a significant tool in the healthcare industry with the potential to change the way care is provided and enhance patient outcomes. With a focus on personalised medicine, ethical issues and problems, future directions and opportunities, real-world case studies, and data privacy and security, this review article investigates the existing and potential applications of deep learning in healthcare. Deep learning in personalised medicine holds enormous promise for improving patient care by enabling more precise diagnoses and individualised treatment approaches. But it's important to take into account ethical issues like data privacy and the possibility of bias in algorithms. Deep learning in healthcare will likely be used more in the future to manage population health, prevent disease, and improve access to care for underprivileged groups of people. Case studies give specific examples of how deep learning is already changing the healthcare industry, from discovering rare diseases to forecasting patient outcomes. To fully realize the potential of deep learning in healthcare, however, issues including data quality, interpretability, and legal barriers must be resolved. Remote monitoring and telemedicine are two promising areas where deep learning is lowering healthcare expenses and enhancing access to care. Deep learning algorithms can be used to analyse patient data in real-time, warning medical professionals of possible problems before they worsen and allowing for online discussions with experts. Finally, when applying deep learning to healthcare, the importance of data security and privacy cannot be understated. To preserve patient data and guarantee its responsible usage, the appropriate safeguards and rules must be implemented. Deep learning has the ability to transform the healthcare industry by delivering more individualised, practical, and efficient care. However, in order to fully realize its promise, ethical issues, difficulties, and regulatory barriers must be solved. Deep learning has the potential to significantly contribute to enhancing patient outcomes and lowering healthcare costs with the right safeguards and ongoing innovation

E-governance can be implemented very effectively using the cloud computing technologies. Security and privacy of data are the major challenges in cloud-based e-governance systems. With proper access control and user revocation schemes, we can protect the privacy and security of data stored in cloud servers used for e-governance. There are a lot of techniques to achieve the controlled access in cloud computing. In some applications of e-government, the users should be allowed to access data, only for a specific period of time. In this paper, we introduce a temporal access control scheme based on attribute-based encryption (ABE), which allows users to access the data according to their access policy and for a specific time period only. Our model includes both the temporal access scheme and a time-based proxy re-encryption scheme to allow a user's access right to expire automatically.

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

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.

This article seeks to address the issue of the regulation of telematics in vehicles. The objective is to navigate the need to protect data privacy and data security while enhancing road safety through telematics. Vehicles telematics devices utilizing analytical and predictive technology can help identify and reduce the risk of dangerous driving. Such devices are a growing tool in the insurance industry and amongst vehicle manufacturers, allowing safe driving to be rewarded whilst dangerous driving can be penalized. Data generated through telematics can also be of use to traffic authorities and governments to help with traffic management and planning. As such, the EU is planning to mandate the use of such devices. The growing use of telematics has, however, faced major data privacy and data security concerns. The article evaluates regulatory responses from the US and EU, highlighting specific European countries. The purpose is to find an effective balance through comparative analysis between driver safety, data privacy and data security.

In the era of big data, international trade has gone through a digital transformation and developed into a new stage of digital trade. The gradual prosperity of digital trade and the increasing contribution of digital elements to the economy of countries and the international community also bring about problems such as data abuse, surveillance, privacy infringement, and regulatory difficulties. In order to adapt to the development trend of digital trade, countries are paying more attention to cross-border data flow and data privacy protection, in order to maintain national security and the data security of various entities. Therefore, this article analyzes the current situation and trends of digital trade, considers the security issues of data privacy from the perspective of different entities, links key issues such as cross-border data flow, data rights, and privacy rights, explores the current status of relevant domestic and foreign legal and regulatory systems, and conducts comparative analysis. The analysis results show that due to different values, there are significant differences in policies regarding digital trade and privacy protection among countries. The commonality is that none of them have established appropriate legal protection systems for data elements and privacy rights to promote the development of digital trade. Under the trend of technological development, data privacy will also be impacted by new technologies through digital trade. Therefore, in response to the expanding demand for data regulation, all entities should improve their privacy protection and cross-border data flow policies and standards from both internal and international perspectives.

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

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.

The privacy and security of medical data is the most important aspect of Health Information System (HIS) as healthcare is totally reliant on data for treatment and research. The main purpose of HIS is to improve treatment processes by providing the latest information about the patient. The healthcare data consist of medical history, path lab records, medication history, diagnosis and treatment details, genetic and sexual information and profession. The privacy of the patient is becoming a concern for healthcare organizations because of the increasing threats and attacks. Moreover, using blockchain-based infrastructure would empower the patients whilst maintaining the security and privacy of the data and providing quality healthcare services. Healthcare providers are moving towards decision-making processes for the advancement in diagnostics, which require the availability and accessibility of accurate data, which is provided by HIS. The healthcare systems will be technically advanced to provide real-time patient monitoring.