Data Protection Research Articles

European health regulations reduce registry-based research

BackgroundThe European Health Data Space (EHDS) regulation has been proposed to harmonize health data processing. Given its parallels with the Act on Secondary Use of Health and Social Data (Secondary Use Act) implemented in Finland in 2020, this study examines the consequences of heightened privacy constraints on registry-based medical research.MethodsWe collected study permit counts approved by university hospitals in Finland in 2014–2023 and the data authority Findata in 2020‒2023. The changes in the study permit counts were analysed before and after the implementation of the General Data Protection Regulation (GDPR) and the Secondary Use Act. By fitting a linear regression model, we estimated the deficit in study counts following the Secondary Use Act.ResultsBetween 2020 and 2023, a median of 5.5% fewer data permits were approved annually by Finnish university hospitals. On the basis of linear regression modelling, we estimated a reduction of 46.9% in new data permits nationally in 2023 compared with the expected count. Similar changes were neither observed after the implementation of the GDPR nor in permit counts of other medical research types, confirming that the deficit was caused by the Secondary Use Act.ConclusionsThis study highlights concerns related to data privacy laws for registry-based medical research and future patient care.

Artificial Intelligence in Education Management: Opportunities, Challenges, and Solutions

Education management, as an integral part of management science, encompasses core functions such as organizing, planning, coordinating, and controlling educational resources, which closely align with the management concepts used in business administration. Whether in the field of education or business, managers need to improve organizational performance through effective strategy development, resource allocation, and decision-making support. The emergence of Artificial Intelligence (AI) has profoundly transformed several industries, and its impact on education is progressively gaining prominence.This paper explores the opportunities, challenges, and solutions associated with the integration of AI in the education sector. AI offers substantial benefits such as personalized learning experiences, automated educational management, and global access to shared educational resources, revolutionizing the traditional educational landscape. However, its application also presents significant challenges, including concerns over data privacy, the widening of the digital divide, and the evolving role of teachers.Through an analysis of both opportunities and challenges, this paper emphasizes the need for strategic measures to ensure the ethical and equitable use of AI in education. These measures include stronger policy frameworks for data protection, comprehensive teacher training programs for AI integration, and the promotion of global cooperation to bridge technological gaps in underdeveloped regions. Furthermore, the paper projects the potential future developments in AI-driven education, highlighting the importance of balancing technology with human interaction in the learning environment.By addressing both the benefits and risks associated with AI, this paper provides actionable insights for educators, policymakers, and technologists, aimed at creating a more inclusive, efficient, and innovative educational system. Future research should continue to examine AI's ethical implications and strive for solutions that ensure education remains accessible and fair in an increasingly AI-driven world.

Perception towards electronic health records & uptake of digital health IDs among the urban residents in northern India: A mixed methods study.

Background & objectives Ayushman Bharat Digital Mission (ABDM) envisages a unique digital health ID for all citizens of India, to create electronic health records (EHR) of individuals. The present study assessed the uptake of Digital Health IDs by the patient and general population, their attitude toward EHR, and explored the barriers to digital ID and utilizing electronic health records services. Methods A concurrent explanatory mixed methods study was undertaken in Chandigarh, India, with an analytical cross-sectional design as a quantitative part and a qualitative descriptive study. The study participants were 419 individuals aged ≥18 yr who attended the urban primary healthcare centre (n=399) and the community-based screening camps (n=20) between July 2021 and January 2022. Latent Class Analysis (LCA) was undertaken to identify hidden sub-population characteristics. In-depth interviews were done to identify the barriers to health ID uptake. Results The digital health ID uptake rate was 78 per cent (n=327). Among the study participants, those who were aware of EHR, those who wanted a national EHR system, those who were confident with the government on EHR security, and those who were willing to make national EHR accessible for research showed significantly higher digital health ID uptake than their counterparts. The themes identified under barriers of uptake from the qualitative interviews were lack of awareness, technology-related (including digital literacy) and utility-related. Interpretation & conclusions Increasing EHR awareness, digital health literacy, and enacting data protection laws may improve the acceptance of the digital health ecosystem in India.

Цифровой политический маркетинг: использование социальных сетей и Big Data для управления политическими предпочтениями

The article discusses the features of the use of digital political marketing as a special technology of political PR. The authors of the study note that digital political marketing has become a favorite method of promoting political products due to the wide opportunities provided by multimedia platforms for public and private communication on the Internet, as well as Big Data technologies, with which you can analyze an incredibly large amount of all kinds of data about users. Based on these data, the course of the political process is effectively adjusted and the political preferences and views of the online electorate are implicitly changed. The authors examine in detail the possibilities of social networks for promoting PR campaigns, analyze ways to collect data about users and note the risks posed by technological innovations of the digital age for political democracy. In conclusion, the authors conclude that in order to achieve effective interaction between political actors and citizens on the Web, it is necessary to maintain a balance between innovative methods of voter engagement and ensuring their rights to privacy and personal data protection in a risky digital era.

CYBERCRIME CHALLENGES: EXPERIENCE OF INTERNATIONAL COOPERATION

The study focuses on the problem of countering cybercrime in the context of the experience of international cooperation. The trend in the process of ongoing transformation from a post-industrial to an information society stands out: global informatization and digitalization of processes lead to the threat of interference in personal life, data theft, and violation of privacy. The analysis of international legal acts made it possible to conclude the need to expand cooperation between states in the fight against cybercrime under the auspices of the UN based on programmatic, strategic acts of international legal regulation of the relevant range of relations. The importance of creating and developing organizational and legislative measures to combat cybercrime, improving interaction between the competent authorities of various states, as well as the need to develop and adopt universal conventions to combat cybercrime, is noted. The authors of the article state that the large-scale growth in the volume of information transfer causes the need to ensure the maximum level of data protection, both by the interested ICT users themselves and by governments - guarantors of state information security and stability. The international legal framework provides a solution to the problem in three areas: reducing discrepancies between national laws, introducing new powers of bodies, and promoting international cooperation. It is stated that in the Republic of Kazakhstan, there are no comprehensive studies on combating cyberterrorism and cybercrime as phenomena that include the entire range of crimes that are committed today on the Internet. The study fills this gap: the activities of the legislative and law enforcement bodies of the Republic of Kazakhstan are analyzed. The authors focus on such positive aspects as the creation of the Ministry of Internal Affairs of the Republic of Kazakhstan in April 2003 of the "K" department to intensify the fight against a wide range of crimes related to computer and Internet technologies, including cyberbullying, the production of counterfeit video discs, the dissemination of information that contributes to extremism, terrorism, cruelty, and violence. Chapter 7 of the Criminal Code of the Republic of Kazakhstan, devoted to criminal offenses in the field of informatization and communications, was subjected to scientific turnover; Cybersecurity Concept ("Cyber Shield of Kazakhstan"); a pilot project of 2023, which launched a special cyber police unit "Cyberpol Strike Task Force" to protect citizens and businesses of the country from cyber threats.

Evaluating the Sufficiency of the data protection act 2023 in the age of Artificial Intelligence (AI): A comparative case study of Nigeria and the USA

The rapid expansion of artificial intelligence (AI) has become a pivotal force in shaping modern society, drawing comparisons to the tech boom of the late 1990s and early 2000s. With AI applications ranging from military and defence systems to corporate tools and household devices, its transformative potential is undeniable. This paper examines the development and regulation of AI on a global scale, focusing on the legislative frameworks in Nigeria and the USA. Specifically, it evaluates the sufficiency of Nigeria's Data Protection Act 2023 in addressing the unique challenges posed by AI. By comparing Nigeria’s approach with that of the USA, the paper highlights key regulatory gaps, the distinction between AI and robotics, and the importance of establishing a legal personality for AI systems. The comparative analysis offers insights into how both countries are preparing for the future of AI, emphasizing the need for early legal intervention to ensure safe and ethical AI integration. Recommendations are provided for policymakers to strengthen regulatory mechanisms in both nations, ensuring they are equipped to handle the rapid evolution of AI technology.

The Privacy/Antitrust Curse: Insights from GDPR Application in Competition Law Proceedings

In response to the emergence of large online platforms whose business model revolves around the collection and processing of personal data, it is usually suggested that competition and data protection are synergistic, hence requiring an integrated approach. In this respect, Europe represents the testing ground for evaluating how privacy breaches may inform antitrust investigations. Indeed, the General Data Protection Regulation (GDPR) on the one side and the German Facebook antitrust decision on the other side are considered the benchmarks of this new stance aimed at linking market power and data power. This paper tests the concrete viability of such an approach, analyzing how data protection rules and principles have been applied in antitrust proceedings by the European Commission and national competition authorities. Notably, the paper aims at demonstrating the fallacy of the narrative which describes the relationship between privacy and antitrust in terms of synergy and complementarity. Furthermore, the paper maintains that the principles recently affirmed by the European Court of Justice in Meta do not appear conclusive in addressing the issue. The strategic use of privacy as a business justification to pursue anticompetitive advantages currently addressed in the numerous Apple ATT investigations indeed shows the tension between these areas of law. Therefore, rather than strengthening the antitrust enforcement against gatekeepers and their data strategies, the inclusion of privacy harms in antitrust proceedings turns out to be a potential curse for competition authorities, providing players with an opportunity for regulatory gaming to undermine the antitrust enforcement. JEL Codes: D83, K21, L12, L4

Cómo entrenar tu dragon: developing a module to build up AI literacy of HE lecturers

This case study summarised the findings from the first academic presentation of the module ‘Cómo entrenar tu dragon (How to train your dragon)’, which is part of the development and training programme offered by the Universidad de Las Palmas de Gran Canaria to new and experienced teaching staff. The module aims to equip participants with a theoretical understanding and practical skills to use Generative AI (GenAI) multimodal languages to enhance learning and to develop a critical awareness of the umbrella term ‘AI literacy’. The module covers topics such as GenAI and data protection, copyright issues, regulatory frameworks, getting familiar with GenAI large language models (LLM) and the impact of GenAI on assessment. The case study provides information on the type of learning activities undertaken by participants and the final summative assessment. Participants found the module insightful, giving it a total score of 4.813 out of 5 on a Likert scale. Participant feedback suggests that institutional guidance and policies are needed to introduce GenAI tools at the Universidad de Las Pal mas de Gran Canaria so they can use GenAI tools confidently with their students.

Blockchain Based Secure File Transfer System with Password Protection

The need of secure file transfer systems is higher nowadays. In order to enhance the security and privacy of private information transferred over the internet, our paper presents a blockchain-based secure file transfer system (BSFTS) with password protection. This system takes advantage of the decentralized and immutable properties of blockchain technology to ensure that every file transfer is Safe and Secure, offering verifiable audit trails that can minimize the threats associated with data tampering and unauthorized access. Through forcing people to use strong, unique passwords for decryption as well as encryption, the combination of password protection further secures files by ensuring that only specified people may access the information. In addition to enhancing data confidentiality, this dual-layer strategy increases defences against possible cyberthreats, including phishing and brute force attacks. Research shows that the blockchain-based secure file transfer system with password protection far exceeds standard file transfer techniques in terms of security and user experience. The results illustrate that password protection and blockchain technology work together to build a robust framework for secure file sharing. For companies trying to enhance their data protection plans in the face of changing security threats in the world of technology, this study provides helpful data

Assessment of the Effectiveness of the E-Government System in Camarines Norte Provincial Hospital

The research focuses on evaluating the effectiveness of the e-government system at Camarines Norte Provincial Hospital. It assesses the system's performance using the System Evaluation Questionnaire aligned with the ISO 25010-2011 system/software quality model indicators, which include functionality, reliability, usability, performance efficiency, compatibility, portability, and security. The study's findings indicate that iHOMIS performs exceptionally well at Camarines Norte Provincial Hospital, particularly excelling in functionality, reliability, usability, and security. This suggests that the system is robust, user-friendly, and meets users' needs effectively, with users expressing confidence in the system's data protection and privacy measures.However, the results for performance efficiency, compatibility, maintainability, and portability are satisfactory but suggest areas for improvement. Overall, the findings point to a positive user experience at Camarines Norte Provincial Hospital, although there is potential for further enhancements to achieve consistent excellence across all evaluated criteria. The study concludes that iHOMIS is well-suited to meet the e-governance needs of government facilities like Camarines Norte Provincial Hospital. The successful implementation of iHOMIS at CNPH demonstrates the significant impact of the Department of Health's initiative in improving hospital operational efficiency and patient outcomes. CNPH's digital transformation journey serves as a model for other government healthcare facilities aiming to enhance patient outcomes through digital means. The insights from this study are expected to be pivotal in advancing e-government initiatives nationwide.

Requirements and their implementation for an intelligent decision-making system for the identification and classification of objects of cultural value

The article considers the problem of development and analysis of requirements and ways of their implementation for an intelligent decision support system for identification and classification of objects of cultural value. The work analyzes both general standards of software development and specialized standards relating to intelligent systems, modern methodologies and practical instructions are also taken into account. The work defines and analyzes such system requirements as: speed of processing large volumes of data, accuracy of identification, integration with other information systems and databases, data protection and information security, scaling, adaptation to new types of data and object behavior models, interface user, processing and analysis of various types of data, cross-platform and forecasting based on available data to support decision-making. The article also defines the complexity and complexity of the approach to solving the given task, the prospects and ways of further research of this subject area are determined.

Future Trends and Technological Innovations of Private AI Deployment

As data protection laws like GDPR tighten, companies must ensure their AI systems comply with regulations, leading to widespread use of encryption, federated learning, and differential privacy to protect data. Privatized AI deployment will transform enterprise operations by integrating AI with core business processes, automating and enhancing efficiency and decision-making. For instance, manufacturing firms can use privatized AI for intelligent monitoring and predictive maintenance, reducing costs and improving quality. The market landscape will shift as companies reevaluate technology and service providers, favoring those offering comprehensive privatization solutions for system security and stability. The growing demand for AI expertise will also drive educational institutions to offer more AI-related courses and training. In summary, the trends in privatized AI deployment will deeply affect business models and market structures, necessitating that companies stay abreast of technological advancements and actively embrace privatized AI opportunities for sustained innovation and growth.

Keeping medical information safe and confidential: a qualitative study on perceptions of Israeli physicians

BackgroundPatients expect their information to remain confidential, and physicians have a legal and ethical obligation to keep it this way. Confidentiality is not just a legal requirement but a crucial element in establishing trust between patients and healthcare providers. Patients must feel confident that their personal and medical information is kept confidential and shared only with those who need to know. Previous studies have primarily concentrated on patients’ perceptions of medical confidentiality, data privacy, and data protection issues. However, research on the practical practices and perceptions of medical confidentiality among hospital physicians is scant, underscoring the need for a deeper understanding of this critical issue.MethodsThrough qualitative methods and as part of a large-scale study on aspects of patient dignity and responsiveness in Israel, physicians shared their views and practices on managing medical information.ResultsThe study revealed the practical challenges physicians face in upholding various aspects of data protection within hospital settings. These challenges, strategies, and deviations from data protection principles that physicians discussed are of significant practical relevance. The importance of patient consent and the practical measures for safeguarding patient information were also highlighted. While physicians acknowledged the importance of protecting patient information, they also grappled with the realities of doing so in a complex healthcare environment. In future healthcare policies, it is critical to ensure robust measures are in place to safeguard and uphold medical confidentiality. These can include specific measures to increase compliance, such as regularly monitoring compliance with confidentiality policies, producing safe and anonymous channels to voice concerns, and enforcing consequences for any breaches to ensure accountability.ConclusionsWhile protecting medical information has emerged as an important goal, it is equally crucial to strike a balance between the need to share information to advance and provide quality medical care. Physicians and policymakers must navigate this delicate balance. Additionally, organizations should strengthen compliance to enhance their monitoring and enforcement of confidentiality policies. Ineffective implementation of medical confidentiality leads to theoretical guidelines that do not translate effectively into practice.

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.

Biobanking, digital health and privacy: the choices of 1410 volunteers and neurological patients regarding limitations on use of data and biological samples, return of results and sharing

BackgroundThe growing diffusion of artificial intelligence, data science and digital health has highlighted the role of collection of data and biological samples, thus raising legal and ethical concerns regarding its use and dissemination. Further, the expansion of biobanking, from the basic collection of frozen specimens to the virtual biobanks of specimens and associated data that exist today, has given a revolutionary potential on healthcare systems, particularly in the field of neurological diseases, due to the inaccessibility of central nervous system and the need of non-invasive investigation approaches. Informed Consent (IC) is considered mandatory in all research studies and specimen collections, and must specifically take into account the ethical respect to the individuals to whom the used biological material and data belong.MethodsWe evaluated the attitudes of patients with neurological diseases (NP) and healthy volunteers (HV) towards the donation of biological samples to a biobank for future research studies on neurological diseases, and limitations on the use of data, related to the requirements set by the General Data Protection Regulation (GDPR). The study involved a total of 1454 subjects, including 502 HVs and 952 NPs, recruited at Santa Lucia Foundation IRCCS, Rome, from 2020 to 2024.ResultsWe found that (i) almost all subjects agreed with the participation in biobanking (ii) and authorization to genetic studies (HV = 99.1%; NP = 98.3%); Regarding the return of results, (iii) we found a statistically significant difference between NP and HV, the latter preferring not to be informed of potential results (HV = 43%; NP = 11.3%; p < 0.0001); (iv) a small number limited the sharing inside European Union (EU) (HV = 4.6%; NP = 6.6%), whereas patients were more likely to refuse transfer outside EU (HV = 7.4%; NP = 10.7% p = 0.05); (v) nearly all patients agreed with the use of additional health data from EMR for research purposes (98.9%).ConclusionsConsent for the donation of material for research purposes is crucial for biobanking and biomedical research studies that use biological material of human origin. Here, we have shown that choices regarding participation in a neurological biobank can be different between HVs and NPs, even if the benefit for research and scientific progress is recognized. NP have a strong interest in being informed of possible results but limit sharing of samples, highlighting a perception of greater individual or relative benefit, while HV prefer a wide dissemination and sharing of data but not to have the return of the results, favoring a possible benefit for society and knowledge. The results underline the need to carefully manage biological material and data collected in biobanks, in compliance with the GDPR and the specific requests of donors.

Determinants of physical activity commitment in adolescents and young adults with cancer: sociological protocol for a hospital-based mixed methods study (ETAPE-AJA)

IntroductionAdolescents and young adults (AYA) with cancer undergo physical transformations due to disease and treatments occurring alongside puberty and adolescence. Although physical activity is recommended for its benefits, its practice...

Federated Learning Frameworks for Secure and Decentralized Authentication

Federated Learning (FL) is a cutting-edge machine learning approach that enables multiple or edge devices to train models collaboratively without sharing sensitive data. This approach not only ensures the privacy and security of data by keeping it localized but also promotes the collective improvement of machine learning models across various participants. A systematic literature review explored integrating Blockchain technology with federated learning. Blockchain's potential to address existing security and privacy vulnerabilities in traditional federated learning systems is analyzed in depth. One of the key benefits of combining Blockchain with FL is enhanced protection against potential attacks, such as data tampering or unauthorized access. The study also examines how Blockchain-based federated learning systems can offer better records and rewards management, contributing to fairer and more transparent systems. In addition, Blockchain's role in improving verification and accountability within federated learning frameworks has been critically evaluated. By integrating Blockchain, federated learning can achieve higher levels of trust and security in collaborative machine-learning processes. The latest research highlights innovative Blockchain-based methods that tackle these challenges, ensuring robust privacy and security measures. Overall, this approach represents a significant advancement in distributed machine learning, aligning with contemporary needs for data protection and collaborative efficiency.

General Data Protection Regulation, Right to Be Forgotten, Blockchain Technology and Human Rights

General Data Protection Regulation, Right to Be Forgotten, Blockchain Technology and Human Rights

Impact of increasing post-filter ionised calcium target on regional citrate anticoagulation efficacy in ICU continuous renal replacement therapy: the non-inferiority randomised controlled Ca-CIBLE protocol

IntroductionContinuous renal replacement therapy (CRRT) is a critical therapeutic intervention for patients with severe acute kidney injury in intensive care. However, premature filter clotting remains a significant challenge during CRRT,...

Quantifying the impact of flexibility asset location on services in the distribution grid: Power system and local flexibility market co-simulation

This research investigates the effectiveness of incorporating locational sensitivity factors into local flexibility market clearing mechanisms for effective congestion management and voltage regulation in distribution grids. A centralized local flexibility market optimization model is developed that considers technical and economic constraints. The study aims to explore the requirements for data availability, data quality, and reliable data exchange that can facilitate a broader range of flexibility services, thereby promoting the development of a local flexibility market. Sensitivity factors, including power transfer distribution factors, voltage sensitivity coefficients and transformer sensitivity coefficients, are used to quantify the impact of flexibility asset locations on congestion management and inform clearing rules. These static metrics are insufficient for establishing effective local flexibility market clearing rules when flexibility is procured by distribution system operators. The approach considers the state of the grid when calculating the sensitivity coefficients, which leads to a more accurate evaluation of flexibility bids, especially with regard to the impact of location on congestion management. The proposed mechanism for clearing the local flexibility market assumes continuous communication between the proposed local flexibility market operator and the distribution system operator for dynamic, iterative market clearing, which ensures the protection of grid data and a more accurate bid evaluation. The study demonstrates that the inclusion of locational information significantly increases the effectiveness of the proposed local flexibility market-based congestion management. The developed simulator for the proposed local flexibility market provides valuable insights into the interaction between the proposed local flexibility market and the distribution grid. The research results, derived from selected use cases, emphasize the importance of location-based sensitivity factors in the proposed local flexibility market clearing for distribution grids. The proposed approach offers a promising solution for optimizing congestion management and voltage regulation while ensuring efficient integration of distributed energy resources into distribution grids.