Privacy Considerations Research Articles

Data Commons Platforms: Addressing Challenges and Opportunities in Large-Scale Biomedical Data Integration and Analysis

Abstract: Data Commons Platforms are emerging as transformative tools in the landscape of medical research, offering a centralized, accessible, and scalable infrastructure for managing, analyzing, and sharing vast amounts of biomedical data. This article provides a comprehensive examination of these platforms, exploring their core functionalities, benefits, and challenges in the context of advancing medical discovery. We discuss how Data Commons Platforms accelerate research by facilitating datadriven hypothesis generation, enhancing collaboration among researchers, improving data accessibility, and ensuring data interoperability. The article also presents a reference architecture for these platforms, detailing the key components that enable their powerful capabilities. Furthermore, we address critical challenges such as data privacy, quality standardization, and ethical considerations, while also highlighting opportunities for future development. Through case studies and an analysis of emerging trends, this work demonstrates the potential of Data Commons Platforms to revolutionize biomedical research, paving the way for more efficient, collaborative, and impactful scientific discoveries in the era of big data and precision medicine.

Beyond virtual boundaries: the intersection of the metaverse technologies, tourism, and lifelong learning in China’s digital discourse

In the evolving landscape of digital education, the metaverse emerges as a revolutionary platform, particularly within the realm of tourism, viewed through the lens of lifelong learning. This study delves into the integration of the metaverse in the tourism and education sectors in China, aiming to uncover the intricate dynamics of public perceptions and the educational potential of this digital innovation. This research utilizes semantic network and sentiment analysis of social media discussions on China’s Weibo platform to provide a detailed understanding of sentiments toward the metaverse’s impact on tourism education. Semantic Network Analysis explores the complex dialog surrounding the metaverse in the context of lifelong learning. The findings reveal a balanced mix of enthusiasm for the immersive educational opportunities afforded by the metaverse and concerns regarding data privacy, digital addiction, and ethical considerations. It is evident from this duality that a balanced approach is necessary when the metaverse is integrated into tourism and learning while being sensitive to technological opportunities and ethical challenges. The metaverse could be an immersive, interactive learning environment endorsed by modern educational theories, supporting active learning through experience. The metaverse will provide valuable assistance to tourism education, which will help enhance this field through the simulation of real tourism for students to learn, which will foster cultural understanding and meet the demands of a globalized tourism industry. It further recommends that policies and practices should be formulated to ensure equal access, respect for the privacy of every participant, and the ethical use of the technology. This study contributes to the analysis of the potentials and challenges of the metaverse in the tourism and learning industry. It highlights the metaverse as an ‘emerging tool for lifelong learning’ and offers insights into how educators, policymakers, and industry leaders, all aiming at the integration of digital innovations in education, should exploit the metaverse.

Exploring advancements and emerging trends in robotic swarm coordination and control of swarm flying robots: A review

Swarm Robotics (SR) is an interdisciplinary field that is rapidly advancing to address complex industrial challenges. This paper provides a comprehensive review of recent advancements and emerging trends in SR, with a specific focus on the coordination and control of Swarm Flying Robots (SFRs). The motivation behind this review is to explore scalable and robust solutions for SFRs to enhance their performance and adaptability across various applications. Key objectives include examining the characteristics and essential behaviors of SR, analyzing the challenges and so lutions for implementing SR in Flying Robots (FRs), and highlighting current and future research directions. The review delves into critical areas such as multiple robot path planning, Swarm Intelligence (SI), combinatorial optimization, and formation flying using SFR. Special attention is given to coordination and control techniques, including formation control in GPS-denied environments, to underscore their significance in advancing SR. The paper also addresses ethical, privacy, and security considerations, emphasizing the importance of responsible practices in SR development. Major takeaways from this review include the identification of key technical challenges and potential solutions in SFR, the exploration of SI algorithms, and the future research directions necessary for fully realizing the potential of SR technologies. By offering detailed insights into state-of-the-art research and its industrial implications, this paper serves as a foundational guide for future studies in the dynamic and promising domain of swarm robotics.

Developing a Framework for Safe AI Model Development on Sensitive Healthcare Data

The Dementias Platform UK (DPUK) Data Portal holds over 60 cohort datasets from over 3 million participants with a range of multi-modal data including neuroimaging and genomics. This has meant we have seen an increasing interest in the development of AI models with the potential for clinical implementation. However, this presents a unique challenge to disclosure control when it comes to assessing these AI models for release due to the risk of attacks such as membership inference, model inversion or even just vulnerabilities in the models like overfitting. This is why we hosted a series of workshops bringing together members of the public, expert researchers, and data owners across the UK to build a framework for allowing the safe development and release of AI models trained on sensitive healthcare data. From this, we have put together recommendations and guidelines for the use of privacy-preserving techniques in AI models to protect patient privacy, and to allow the safe deployment of these models outside of trusted research environments. We also identified the unique challenges to privacy and implementation of privacy-preserving techniques in AI models regarding the use of complex data such as neuroimaging and genomics. This framework has created a path forward for supporting safe AI model development which takes into consideration rapidly evolving ethical, legal and privacy considerations.

Digital Afterlives: Imagining Effective Policies and Regulations for Digital Remains

The digital platforms we engage with daily are brimming with the digital traces of the dead. These digital remains – or the photos, videos, and text messages left behind by deceased users (Lingel 2013), have given rise to the digital death industry – an umbrella term for online platforms that offer services such as online memorialisations, virtual funerals, and graves, andinteractions with avatars of the deceased through chatbots and virtual reality (Öhman and Floridi 2017). Mourners turn to these digital infrastructures to express their grief and experience the continued presence of the deceased through their digital remains (Kasket 2020). Yet the presence of digital remains and the digital death industry presents several policy concerns related toprivacy, digital dignity, the question of access and ownership, and the potential for commodifying deceased user data (Kasket, 2019). Without effective policies and regulations that provide guidelines for service providers and internet users, the dead are not afforded privacy rights nor considerations for their digital dignity and protections for their postmortem data. Left exposed and with no legal safeguards in place, digital remains are vulnerable to offences with tremendous emotional implications for the bereaved.Guided by information ethics as a theoretical framework exploring questions related to creating, accessing, and collecting digital data (Bruneault et al., 2023), my lightning talk asks us to imagine constructing potential policies and regulations that serve to protect our posthumous digital footprint.

Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey

The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML’s data analytics and predictive capabilities to enhance the intelligence of IoT systems, while blockchain provides a secure, decentralized framework that ensures data integrity and trust. The synergy of these technologies not only optimizes urban management but also fortifies security and privacy in increasingly connected cities. This survey explores the transformative potential of ML-driven blockchain-IoT ecosystems in enabling autonomous, resilient, and sustainable smart city infrastructure. It also discusses the challenges such as scalability, privacy, and ethical considerations, and outlines possible applications and future research directions that are critical for advancing smart city initiatives. Understanding these dynamics is essential for realizing the full potential of smart cities, where technology enhances not only efficiency but also urban sustainability and resilience.

Artificial intelligence for the study of human ageing: a systematic literature review

As society experiences accelerated ageing, understanding the complex biological processes of human ageing, which are affected by a large number of variables and factors, becomes increasingly crucial. Artificial intelligence (AI) presents a promising avenue for ageing research, offering the ability to detect patterns, make accurate predictions, and extract valuable insights from large volumes of complex, heterogeneous data. As ageing research increasingly leverages AI techniques, we present a timely systematic literature review to explore the current state-of-the-art in this field following a rigorous and transparent review methodology. As a result, a total of 77 articles have been identified, summarised, and categorised based on their characteristics. AI techniques, such as machine learning and deep learning, have been extensively used to analyse diverse datasets, comprising imaging, genetic, behavioural, and contextual data. Findings showcase the potential of AI in predicting age-related outcomes, developing ageing biomarkers, and determining factors associated with healthy ageing. However, challenges related to data quality, interpretability of AI models, and privacy and ethical considerations have also been identified. Despite the advancements, novel approaches suggest that there is still room for improvement to provide personalised AI-driven healthcare services and promote active ageing initiatives with the ultimate goal of enhancing the quality of life and well-being of older adults.Graphical abstractOverview of the literature review.

Enhancing privacy protections in national identification systems: an examination of stakeholders’ knowledge, attitudes, and practices of privacy by design

Privacy by Design (PbD) is a well-known concept that aims to provide a high level of protection for privacy throughout the entire life cycle of systems development. Despite the considerable attention from stakeholders such as researchers, government agencies, and system suppliers, the widespread adoption of PbD faces obstacles due to a lack of knowledge, insufficient awareness of PbD benefits, and the absence of specific implementation guidelines. In this study, stakeholders are identified primarily as diverse participants from government agencies and system suppliers engaged in National Identification Systems (NIDS). Specifically, government agencies representing regulatory bodies and administrators of NIDS, setting the legal framework that governs the NIDS’s privacy aspects. The NIDS system suppliers includes private companies playing a crucial role in the development and implementation of NIDS with a focus on privacy considerations. Through the perspectives of NIDS stakeholders, this study aimed to examine the Knowledge, Attitudes and Practices (KAP) of PbD principles and its integration in NIDS. A survey involving 203 participants from government agencies and NIDS system suppliers engaged in NIDS development was conducted. Subsequently, a focus group discussion was held with 11 members to provide qualitative insights into the KAP of PbD. The survey results revealed a significant correlation between attitudes and practices but a weak correlation between knowledge and attitudes or practices. The focus group discussion assured these findings, emphasizing the role of positive attitudes in facilitating PbD practices and highlighting knowledge-practice gaps. In conclusion, this study offers tailored recommendations for improving the integration of PbD in NIDS development. The recommendations includes strategies such as developing training programs, establishing clear guidelines and standards and creating awareness campaigns.

Indoor Person Position Estimation with Channel State Information with Privacy Consideration

Indoor Person Position Estimation with Channel State Information with Privacy Consideration

Virtuell virkelighet, virkelig mestring: Virtual Reality som verktøy i behandling av psykoselidelser

Virtual reality (VR) is emerging as an innovative tool in psychotherapy. In this article, we review the evidence base for the use of VR in therapy, focusing on the treatment of psychotic disorders. We present a fictional case study based on real-life patient pathways from several psychosis treatment units at Oslo University Hospital, showing how VR can be used to address anxiety and avoidance behaviours in patients with psychotic disorders. Furthermore, we introduce RecoVRy, a novel Norwegian intervention combining VR and artificial intelligence (AI) to help individuals with psychotic disorders manage everyday anxiety. Although integrating VR and AI holds considerable potential, we also discuss challenges related to privacy and ethical considerations. We conclude that although VR therapy probably does not represent a universal solution, the technology constitutes a valuable supplement that can enrich therapeutic practices and potentially contribute to significant health and socioeconomic savings. Finally, we emphasise the need for further research to fully realise this potential. Keywords: Virtual reality, VR, virtual reality exposure therapy, VRET, artificial intelligence, AI, psychosis, schizophrenia

Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy.

The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19 mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic. This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explores people's contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextual integrity. Specifically, this research seeks to identify the factors that impact people's acceptance of data sharing and data retention practices in various social contexts. A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674) on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts that COVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people's acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results. Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios. This study showed that people's acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmission principle). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.

A moment of turbulence: Privacy considerations in the pivot to distance learning during COVID-19 in higher education in Estonia, France, and Israel

A moment of turbulence: Privacy considerations in the pivot to distance learning during COVID-19 in higher education in Estonia, France, and Israel

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

BackgroundWith the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. ObjectivesThis study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. MethodsWe employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. ResultsSix themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. ConclusionsThe findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Pornography, sexual privacy and copyright

This article proposes a new paradigm in the consideration of privacy in pornographic works in copyright enforcement actions. It focuses particularly on attempts to threaten individuals with copyright infringement action based on a speculative invoicing model. We approach this issue from the perspective of the right to sexual privacy of alleged infringers, which, as we argue, is particularly pertinent for pornographic works. The courts in England and Wales have broadly recognised the role of individual privacy and embarrassment caused to alleged infringers in the leading cases of Golden Eye and subsequently in Mircom, but the law remains unclear with no real recognition of, or meaningful mechanisms in place to address, the underlying issues. The article points out that this is due to a fundamental lack of appreciation of sexual privacy at a conceptual level in the context of consumption of pornography in the internet age, and consequent failure to consider this in copyright enforcement proceedings. We argue that the law should achieve a balance between the right holder's interest and the sexual privacy of alleged infringers, and copyright enforcement actions need to be approached with this in mind. This calls for a fundamental reconceptualisation of the right to privacy, and we call upon the courts to recognise and balance the sexual privacy rights of the alleged infringers of copyright in pornographic works with the interests of the right holders in certain copyright enforcement actions to achieve fair and equitable outcomes.

Modelling occupant behaviour in residential buildings: A systematic literature review

This systematic literature review examines Occupant Behaviour (OB) modelling in residential buildings, focusing on its role in narrowing the energy performance gap and enhancing occupant comfort. It provides an overview of methodologies from simple statistical models to advanced machine learning algorithms. We conducted a chronological literature review on OB modelling in residential buildings, identifying the algorithms and the main remarks and limitations of each study. We also present sample populations for each study, offering a comprehensive view of OB modelling's evolution. Our study shows a trend towards incorporating intelligent technologies like IoT and Artificial Intelligence, highlighting an evolution towards more advanced and precise techniques. The review evaluates studies utilizing monitoring, diary, or survey data to improve behavioural model accuracy, covering aspects such as occupant presence, window manipulation, lighting and shading regulation, thermostat adjustment, and usage patterns of domestic hot water and appliances. The review underscores the importance of standardized data collection and integrating OB into building standards. It advocates for adaptive OB models leveraging IoT data with privacy considerations and calls for comparative analysis of modelling techniques. Future research should develop adaptable occupancy profile repositories and integrated modelling frameworks to address OB complexities.

Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

Unraveling the complexities of financial innovation and digital transformation within banking systems

The objective of this comprehensive review is to examine the intricate relationships among operational efficiency, financial innovation, and digital transformation in the banking sector. Through a systematic literature review spanning from 2000 to 2024, this study synthesizes diverse findings from global scholars. Key findings highlight the interconnectivity of these elements, with AIS adoption improving operational efficiency through data accuracy and timely decision-making. The impact of financial innovation on banking performance is contingent upon regulatory dynamics, emphasizing adaptive strategies. Digital solutions such as big data analytics and blockchain drive socioeconomic progress, bridging gaps in wealth management. Challenges include navigating legacy systems, privacy concerns, and innovation-related risks. Theoretical implications suggest that financial institutions must strategically adopt digital technologies, considering regulatory dynamics and fostering innovation. Robust AIS systems optimize processes, while digital lean operations enhance efficiency. Leveraging digital solutions requires careful consideration of data quality, privacy, and ethical use. The study's importance lies in providing adaptive strategies necessary to manage risks and embrace opportunities in the evolving financial landscape. Ultimately, this study establishes a comprehensive framework for understanding the intricate dynamics of operational efficiency, financial innovation, and digital transformation in modern banking, offering valuable guidance for financial institutions navigating the complexities of the digital era.

A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education

In an era where artificial intelligence (AI) is reshaping educational paradigms, this study explores AI-based chatbot adoption in higher education among students and educators. Employing a Structural Equation Modeling (SEM) approach, the research focuses on developing and validating a comprehensive model to understand the multifaceted factors impacting the acceptance and use of these chatbots. The methodology integrates an extensive literature review, construction of a theoretical model, administration of a detailed questionnaire to a representative sample from the higher education sector, coupled with advanced SEM techniques for data analysis and interpretation. The SEM analysis validates the model's robustness and highlights the relationships between several key factors affecting users' perspectives and chatbots adoption. Results reveal a predominantly positive perception towards AI-chatbots among both students and educators, underscoring the potential to substantially enrich their educational journey. However, it also uncovers critical concerns pertaining to trust, privacy, response bias, and information accuracy. Moreover, the study offers valuable insights into how moderators such as technological proficiency, user roles, and gender influence the adoption model relationships. This emphasizes the need for customizing AI-chatbots deployment to meet the diverse needs of users effectively. Contributing a robust framework for understanding users' perceptions towards AI-chatbots and their adoption patterns, this study offers actionable insights for educational leaders, policymakers, and technology developers. It also lays the groundwork for future research, including longitudinal studies to evaluate the long-term impact of these technologies, investigations into their effect on learning outcomes, and explorations of the ethical and privacy considerations involved.

A perspective on smart universities as being downsized smart cities: a technological view of internet of thing and big data

The integration of internet of things (IoT) and big data technologies is transforming the overall perspective of managing various sectors of modern life; with higher educational sectors being no exception of this transformation. This paper explores the idea of a “smart university” as an extension of the overarching “smart city” framework, emphasizing the blending of IoT and big data technologies within higher education institutions. The study investigates the incorporation of IoT technologies throughout university campuses, including intelligent classrooms, smart infrastructure, and device networking. Moreover, the paper delves into the substantial role played by big data analytics in processing and extracting meaningful insights from extensive data generated by IoT devices in a Smart University. The use of predictive analytics, machine learning algorithms, and data-driven decision-making contributes to personalized learning experiences, adaptive campus management, and proactive maintenance of university facilities. Furthermore, this paper not only emphasizes the potential benefits of deploying IoT and big data in a university setting but also addresses challenges related to security, privacy, and ethical considerations. By embracing a comprehensive approach to technology integration, universities can leverage the capabilities of IoT and big data to establish intelligent, interconnected, and flexible educational environments that align with the broader vision of a smart city.

Using Wearable Technologies to Monitor Physical Activity and Exercise in Patients: A Narrative Review.

With an increasing prevalence of sedentary lifestyles driving a prominent public health crisis, digital health tools such as wearable technologies are revolutionizing clinicians' ability to track physical activity and exercise. Despite their ubiquity in the consumer market, these technologies have not yet been fully incorporated into clinical practice. Though these tools promise efficacy and accessibility, a careful review of the current literature is important to understand the challenges and future promise of clinical implementation. Important considerations of implementation include health maintenance and disease prevention, ease of use by patients and providers, incorporation into the electronic health record, cost considerations, safety, privacy, and ethical considerations. This narrative review describes the recent literature on the implementation of wearable technologies in the prescription of physical activity and exercise. Application of these technologies is promising for this field's future.