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Introduction: Cloud-based telemedicine holds promise for improving healthcare accessibility and delivery, particularly in rural areas of developing countries like Tanzania. However, little is known about its determinants and benefits in such contexts. This study investigates the factors influencing the usage of telemedicine in Mvomero district, Morogoro region, Tanzania, focusing on both supply and demand sides. Method: Using structured interviews and key informant interviews, the study examines various cloud-based telemedicine platforms, including remote monitoring, electronic health records, cloud-based storage, and machine learning algorithms. The study used descriptive statistics to analyze quantitative data, while thematic analysis was used to analyze qualitative data. Results: Results reveal several factors influencing telemedicine usage. On the demand side, perceived benefits (53.96%), technology cost (62.79%), legal practices (62.79%), and resource availability and affordability (49.77%) are crucial. On the supply side, technological innovation (35%) and access to financial resources (43%) play pivotal roles. Environmental and institutional factors such as political willingness (38%) and regulatory support (34%) also impact telemedicine usage. Moreover, results reveal that cloud-based telemedicine platforms in rural healthcare facilities have several benefits including improved access (32.74% to 57.44%), cost efficiency (37.88% to 54.82%), timely consultations (56.83% to 65.21%), health monitoring, and prescription management (43.89% to 75.90%). Private facilities particularly emphasize health monitoring. Conclusion: Adopting telemedicine technologies can revolutionize rural healthcare by providing customized and easily accessible services. Policymakers can use these findings to develop targeted strategies, including subsidized infrastructure, innovative financing models, and clear regulatory frameworks. Clear guidelines on data transfer and privacy are essential to ensure legal compliance and equitable access to telemedicine benefits. Simplifying registration requirements and implementing explicit consent mechanisms are recommended to address data privacy concerns. These measures aim to promote operational efficiency, data safety, and enhanced health outcomes in resource-limited settings. [ABSTRACT FROM AUTHOR]

AbstractThis study aims to understand the challenges in adoption of blockchain technology in higher education institutions in India using the technological-organizational-environmental (TOE) framework. Blockchain brings transparency, efficiency in working systems, and leveraging trust. The benefits of blockchain are multifaceted and might be beneficial to educational institutions. However, the utilization of blockchain technology is presently in its nascent stage within the educational sector in India. This research employed a qualitative methodology involving semi-structured interviews with participants working in higher administration teams and IT teams in private universities in the Delhi-NCR region of India. The responses of the participants were analyzed using thematic analysis. The study found 10 main challenges that were categorized under the three dimensions of the TOE framework.

BackgroundCollaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy.ObjectiveWe aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism.MethodsWe developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets.ResultsWhen one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values

Federated learning (FL) is a distributed machine learning technique that enables multiple decentralized clients to develop a model collaboratively without exchanging their local data. Heightened privacy risks and the recent strict privacy laws make it even more precarious for the gathering and integration of data in a centralized location for full utilization. Federated learning is compatible with established privacy laws like General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), and China’s Cybersecurity Law. Further, there are very few scenarios where centralized, properly labeled, and complete data are available. Federated learning provides a way to solve this problem. As a result, much research has been conducted in several areas within the emerging field of FL. This review paper focuses on decision tree-based FL systems due to their desirable properties of interpretability, parallelism, and high performance. We take a closer look at the motivations, design considerations, tree building algorithms, and security mechanisms used for these systems. We also present the various datasets used in these systems, demonstrated application areas, and the evidence of their benefits. The objective of this paper is to provide an informative overview about the characteristics of FL, privacy and security mechanisms used in them, available open source development frameworks for FL, and the decision tree-based systems developed in FL for researchers in academia and system architects in the industry.

Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the urgent need for effective bias detection and mitigation strategies to build patient trust. The evolving roles of decentralized data sharing, regulatory frameworks, and patient agency are discussed in depth. Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care. [ABSTRACT FROM AUTHOR]

In recent years, large language models (LLMs) have come into view as one of the most transformative developments in the technical domain, influencing diverse sectors ranging from natural language processing (NLP) to creative arts. Their rise signifies an unprecedented convergence of computational prowess, sophisticated algorithms, and expansive datasets, pushing the boundaries of what was once thought to be achievable. Such a profound impact mandates a thorough exploration of the LLMs' evolutionary trajectory. Consequently, this article conducts a literature review of the most impactful scientific works, using the reliable Web of Science (WoS) indexing database as a data source in order to attain a thorough and quality-assured analysis. This review identifies relevant patterns, provides research insights, traces technological growth, and anticipates potential future directions. Beyond mapping the known, this study aims to highlight uncharted areas within the LLM landscape, thereby catalyzing future research endeavors. The ultimate goal is to enhance collective understanding, encourage collaboration, and guide subsequent innovations in harnessing the potential of LLMs for societal and technological advancement. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to analyze the anti-money-laundering (AML) policies in place around the world for digital assets and cryptocurrencies. Design/methodology/approach: This study examines the regulatory frameworks of some of the most prominent international crypto markets and provides an analysis of macro trends in digital asset money laundering legislation. Findings: As widespread use of cryptocurrencies has increased, authorities around the world have begun to focus on the associated money-laundering concerns. Organizations that want to take advantage of investment opportunities related to digital assets must keep an eye on the ever-changing worldwide regulatory landscape. Practical implications: Before entering the digital asset market, potential participants should familiarize themselves with the relevant regulations and processes. In cases where enterprises interface with several international regulators, the complexity of these factors can increase. Originality/value: This essay is written for financial institutions who are considering crypto-related investment opportunities as a means of diversification to better comprehend the worldwide AML regulatory framework pertaining to digital assets. [ABSTRACT FROM AUTHOR]

COVID-19 was an unprecedented pandemic that changed the lives of everyone. To handle the virus’s rapid spread, governments and big tech companies, such as Google and Apple, implemented Contact Tracing Applications (CTAs). However, the response by the public was different in each country. While some countries mandated downloading the application for their citizens, others made it optional, revealing contrasting patterns to the spread of COVID-19. In this study, in addition to investigating the privacy and security of the Canadian CTA, COVID Alert, we aim to disclose the public’s perception of these varying patterns. Additionally, if known of the results of other nations, would Canadians sacrifice their freedoms to prevent the spread of a future pandemic? Hence, a survey was conducted, gathering responses from 154 participants across Canada. Next, we questioned the participants regarding the COVID-19 pandemic and their knowledge and opinion of CTAs before presenting our findings regarding other countries. After showing our results, we then asked the participants their views of CTAs again. The arrangement of the preceding questions, the findings, and succeeding questions to identify whether Canadians’ opinions on CTAs would change, after presenting the proper evidence, were performed. Among all of our findings, there is a clear difference between before and after the findings regarding whether CTAs should be mandatory, with 34% of participants agreeing before and 56% agreeing afterward. This hints that all the public needed was information to decide whether or not to participate. In addition, this exposes the value of transparency and communication when persuading the public to collaborate. Finally, we offer three recommendations on how governments and health authorities can respond effectively in a future pandemic and increase the adoption rate for CTAs to save more lives.

COVID-19 was an unprecedented pandemic that changed the lives of everyone. To handle the virus's rapid spread, governments and big tech companies, such as Google and Apple, implemented Contact Tracing Applications (CTAs). However, the response by the public was different in each country. While some countries mandated downloading the application for their citizens, others made it optional, revealing contrasting patterns to the spread of COVID-19. In this study, in addition to investigating the privacy and security of the Canadian CTA, COVID Alert, we aim to disclose the public's perception of these varying patterns. Additionally, if known of the results of other nations, would Canadians sacrifice their freedoms to prevent the spread of a future pandemic? Hence, a survey was conducted, gathering responses from 154 participants across Canada. Next, we questioned the participants regarding the COVID-19 pandemic and their knowledge and opinion of CTAs before presenting our findings regarding other countries. After showing our results, we then asked the participants their views of CTAs again. The arrangement of the preceding questions, the findings, and succeeding questions to identify whether Canadians' opinions on CTAs would change, after presenting the proper evidence, were performed. Among all of our findings, there is a clear difference between before and after the findings regarding whether CTAs should be mandatory, with 34% of participants agreeing before and 56% agreeing afterward. This hints that all the public needed was information to decide whether or not to participate. In addition, this exposes the value of transparency and communication when persuading the public to collaborate. Finally, we offer three recommendations on how governments and health authorities can respond effectively in a future pandemic and increase the adoption rate for CTAs to save more lives. [ABSTRACT FROM AUTHOR]

In recent years, the way that machine learning is used has undergone a paradigm shift driven by distributed and collaborative learning. Several approaches have emerged to enable pervasive computing and distributed learning in ubiquitous Internet of Things (IoT) systems. Numerous decentralized strategies have been proposed to deal with the limitations of centralized learning, including privacy and latency due to sharing local data, while utilizing distributed computations as a promising substitute to centralized learning. However, such distributed learning schemes come with new security and privacy concerns that should be addressed. Thus, in this paper, we first provide an overview for the emerging paradigms developed for distributed learning. Then, we performed a comprehensive survey for the privacy and security challenges associated with distributed learning along with the presented solutions to overcome them. Furthermore, we highlight key challenges and open future research directions toward implementing more robust distributed systems.

Given the growing installed capacity, wind energy will exert a profound impact on the flexibility of modern energy systems. Wind power forecasting is a practical solution for dealing with the attributed variations and uncertainties, balancing supply and demand, and improving the reliability of the system. To achieve more accurate and generalizable forecast models, comprehensive data sets, supplied by multiple wind farms owing to their spatio-temporal dependencies, are required. In addition, data aggregation/collaboration across many wind farms scattered around a country is difficult, if not impossible, due to complex administrative processes, industry competition, and data privacy and security concerns. This article offers federated learning-based wind energy forecasting as a novel decentralized collaborative modeling method capable of training a single model on data from many wind farms without jeopardizing the privacy or security of data. To this end, rather than sending private data across sites, local model parameters are securely transmitted. A comparison between the proposed private distributed model and non-private centralized and fully private localized models indicates the high performance of the proposed federated learning-based wind power forecasting with 87.96% accuracy. Enjoying the smoothing effect, the higher generalizability of the proposed model with 83.63% accuracy is also substantiated in comparison to localized and centralized approaches while the privacy of the underlying data is preserved.

Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the urgent need for effective bias detection and mitigation strategies to build patient trust. The evolving roles of decentralized data sharing, regulatory frameworks, and patient agency are discussed in depth. Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care.

An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants.

A large number of researchers, academia, government sectors, and business enterprises are adopting the cloud environment due to the least upfront capital investment, maximum scalability, and several other features of it. Despite the multiple features supported by the cloud environment, it also suffers several challenges. Data protection is the primary concern in the area of information security and cloud computing. Numerous solutions have been developed to address this challenge. However, there is a lack of comprehensive analysis among the existing solutions and a necessity emerges to explore, classify, and analyze the significant existing work for investigating the applicability of these solutions to meet the requirements. This article presents a comparative and systematic study, and in-depth analysis of leading techniques for secure sharing and protecting the data in the cloud environment. The discussion about each dedicated technique includes: functioning for protecting the data, potential and revolutionary solutions in the domain, the core and adequate information including workflow, achievements, scope, gaps, future directions, etc. about each solution. Furthermore, a comprehensive and comparative analysis of the discussed techniques is presented. Afterward, the applicability of the techniques is discussed as per the requirements and the research gaps along with future directions are reported in the field. The authors believe that this article’s contribution will operate as a catalyst for the potential researchers to carry out the research work in the area.

This study applies and builds on the Use and Gratification (U&G) theory to explore consumer acceptance of applied artificial intelligence (AI) in the form of Chatbots in online shopping in China. Data were gathered via an anonymous online survey from 540 respondents who self-identified as frequent online shoppers and are familiar with Chatbots. The results of the data analysis provide empirical evidence indicating that utilitarian factors such as the "authenticity of conversation" and "convenience", as well as hedonic factors such as "perceived enjoyment", result in users having a positive attitude towards Chatbots. However, privacy issues and the immaturity of technology have had a negative impact on acceptance. This paper provides both theoretical and practical insights into Chinese attitudes toward Chatbots and may be of interest to e-commerce researchers, practitioners, and U&G theorists. [ABSTRACT FROM AUTHOR]

This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems' data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment. [ABSTRACT FROM AUTHOR]

Machine learning algorithm emerges as a promising approach in energy fields, but its practicality is hindered by data barriers, stemming from high collection costs and privacy concerns. This study introduces a novel federated learning (FL) framework based on XGBoost models, enabling safe collaborative modeling with accessible yet concealed data from multiple parties. Hyperparameter tuning of the models is achieved through Bayesian Optimization. To ascertain the merits of the proposed FL-XGBoost method, a comparative analysis is conducted between separate and centralized models to address a classical binary classification problem in the geoenergy sector. The results reveal that the proposed FL framework strikes an optimal balance between privacy and accuracy. FL models demonstrate superior accuracy and generalization capabilities compared to separate models, particularly for participants with limited data or low correlation features, and offer significant privacy benefits compared to centralized models. The aggregated optimization approach within the FL agreement proves effective in tuning hyperparameters. This study opens new avenues for assessing unconventional reservoirs through collaborative and privacy-preserving FL techniques. • Proposed a federated learning framework to address data scarcity and privacy. • Develop an FL-XGBoost model for effective evaluation of geoenergy potential. • Introduce a secure Bayesian optimization for hyperparameter tuning. • Validated with real-world data, the model achieves an acceptable accuracy-privacy trade-off. [ABSTRACT FROM AUTHOR]

Biobanking provides benefit for future generations by facilitating medical research and subsequent translation and application of research findings. Long-term storage and research involving biological material and associated data necessitate the proper implementation of ethical and legal standards. A key principle includes recognizing informed consent as a crucial element for legitimizing the collection of biological material and data. Furthermore, any collected material and data must be employed exclusively for the research framework that aligns with the explicit consent provided by the participants. Last but not least, data privacy and security are essential in biobanking. This review elucidates chances and limitations of biobanking in the field of allogeneic hematopoietic cell transplantation. We discuss the practical implementation of the requirements, illustrated by the Collaborative Biobank, a collaborative research platform for research in blood cancer. [ABSTRACT FROM AUTHOR]

Big data (BD) analytics is one of the critical components in the digitalization of the oil and gas (O&G) industry. Its focus is managing and processing a high volume of data to improve operational efficiency, enhance decision making and mitigate risks in the workplace. Enhanced processing of seismic data also provides the industry with a better understanding of BD applications. However, the industry still exercises caution in adopting new technologies. The slow pace of technology adoption can be attributed to various causes, from the obstacles to the integration with existing systems, to cybersecurity for defending the BD system against cyber attacks. In some applications using wearable devices, physiological and location-tracking data also causes concerns related to workplace privacy implications. These shortcomings give rise to uncertainties about the practical benefits and effectiveness of applying BD in O&G activities. The objective of this paper is to perform a systematic review of BD analytics within the context of the O&G industry. This paper attempts to evaluate technical and nontechnical factors affecting the adoption of BD technologies. The study includes BD development platforms, network architecture, data privacy implications, cybersecurity, and the opportunities and challenges of adopting BD technologies in the O&G industry.

Healthcare data is very sensitive as many healthcare organizations will be very reluctant to share health data. However, sharing the healthcare data is having many more uses for both the patients as well as the research institutions too. Moreover, the existing Electronic Healthcare Record (EHR) management system will be stored in the central database in the form of plaintext. Whenever the data needs to be accessed from the database, the users will be requesting the required EHRs. However, this mechanism possesses the several challenges such as single point of failure, takes more time for user identification, interoperability issues, data recoverability issues, lack of privacy and security. This paper mainly focuses on providing security for the healthcare data, which can be shared among the various health institutions. Authentication and authorization are provided by establishing multiple certification authorities on the permissioned healthcare blockchain network. In this proposed model data integrity is also achieved by the concept of hashing of the electronic health records rather than storing it directly onto the permissioned healthcare block chain network. [ABSTRACT FROM AUTHOR]

Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacypreserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain. [ABSTRACT FROM AUTHOR]

Data in the educational context are becoming increasingly important in decision-making and teaching-learning processes. Similar to the industrial context, educational institutions are adopting data-processing technologies at all levels. To achieve representative results, the processes of extraction, transformation and uploading of educational data should be ubiquitous because, without useful data, either internal or external, it is difficult to perform a proper analysis and to obtain unbiased educational results. It should be noted that the source and type of data are heterogeneous and that the analytical processes can be so diverse that it opens up a practical problem of management and access to the data generated. At the same time, ensuring the privacy, identity, confidentiality and security of students and their data is a "sine qua non" condition for complying with the legal issues involved while achieving the required ethical premises. This work proposes a modular and scalable data system architecture that solves the complexity of data management and access. On the one hand, it allows educational institutions to collect any data generated in both the teaching-learning and management processes. On the other hand, it will enable external access to this data under appropriate privacy and security conditions. [ABSTRACT FROM AUTHOR]

Mobile health monitoring devices (MHMDs) have become widely integrated into our personal lives as well as healthcare delivery. However, less attention is paid to individual consumer and clinician awareness, knowledge, and agency, in regards to the privacy and security of data from wearable devices. Two cross sectional intervention studies assessed cybersecurity attitudes, risky cyber behaviors, and mobile health monitoring device dependence among undergraduate students and future health practitioners. The video based intervention used in study two was effective in changing attitudes toward cybersecurity (n =165, t-test, p ≤ 0.001) where study one's paper-based intervention was not (n = 200, t-test, p = 0.347). In both studies, improved attitude toward cybersecurity was associated with increased risky cyber behavior (p ≤ 0.001, rs=0.334 and p = 007, rs=0.228, respectively). Attitude was associated with device dependence in study one, but not study two (p ≤ 0.001, rs=0.263 and p = 0.432 respectively). Attitude toward cybersecurity varied by self-identified social class (ANOVA p = 0.005), with those in the upper/upper middle class having a more positive attitude than those in the middle class (p = 0.009) and the working/lower class (p = 0.048). Both studies indicated participants felt the need for more awareness and knowledge of risks, suggested personal experiences were their leading factor in changing cyber behaviors and attitudes, and that consumer knowledge, industry standards, as well as policy change are needed to increase the security of data being generated by wearable health technologies. Health practitioner and consumer awareness and knowledge of data security is a critical issue as personal and identifiable data continues to be commodified. [ABSTRACT FROM AUTHOR]

Background: International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses., Objective: This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses., Methods: A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach., Results: Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level., Conclusions: There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized., (©Rebecca Baines, Sebastian Stevens, Daniela Austin, Krithika Anil, Hannah Bradwell, Leonie Cooper, Inocencio Daniel Maramba, Arunangsu Chatterjee, Simon Leigh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.03.2024.)

In China, one percent of the richest population holds more than one-third of the wealth, while the poorest 25% shares no more than two percent of the total. The country’s rapid economic development has resulted in increasing socio-economic disparities, and a rapidly deteriorating environment. This puts the Chinese citizens, especially the most vulnerable and deprived socio-economic status (SES) groups, at high risks of environmental inequality (EI). In most SES-based EI studies conducted in China, household wealth has often been overlooked, though it potentially serves a good economic indicator to capture the socio-economic effect of environmental change in China. Nevertheless, existing SES databases in China are of low spatial resolution and are insufficient to support fine-grained EI studies at the intra-city level in China. The core research challenge is to develop a representative household wealth proxy in high-spatial resolution for China. This study highlights the research gaps and proposes a new household wealth proxy, which integrates both fine-grained data/features such as daytime satellite imagery and easily accessible wealth indicators such as house prices. We also capitalize on everyday economic activity data retrieved from personal mobile phones and online transaction/social platforms in the composition of our wealth proxy to achieve a higher accuracy in estimating household wealth at fine-grained resolution via machine learning. Finally, we summarize the challenges in improving both the quality and the availability of Chinese socio-economic datasets, while protecting personal privacy and information security during the data collection process for household wealth proxy development in China.

Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Articial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models. [ABSTRACT FROM AUTHOR]

Innovations emerging in the dynamic environment of technological developments lead to new situations in social and economic fields. Cities are one of the important areas where these developments are reflected. Thus, smart cities and big data concepts come up and these developments cause changes in services provided in cities. However, these developments bring along some negativities, as well. The main purpose of this study is to examine the approaches and principles that have come to fore in order to ensure the privacy and security of big data in smart cities. Basically, the answer to this question is sought: what principles, approaches and practices come to the forefront in order to ensure the confidentiality and security of big data that is an important part of smart cities? The answer is sought through the relevant literature, legal regulations and international decisions. According to the general results of the study, it is understood that there are not any consensual standards on data security at global level. However, some regulations draw attention. At this point, the "General Data Protection Regulation (GDPR)" which came into force in 2018 is important. However, some principles and approaches are gaining importance. At the end of the study, some suggestions about policies are made according to the results obtained. [ABSTRACT FROM AUTHOR]

A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.

Data in the educational context are becoming increasingly important in decision-making and teaching-learning processes. Similar to the industrial context, educational institutions are adopting data-processing technologies at all levels. To achieve representative results, the processes of extraction, transformation and uploading of educational data should be ubiquitous because, without useful data, either internal or external, it is difficult to perform a proper analysis and to obtain unbiased educational results. It should be noted that the source and type of data are heterogeneous and that the analytical processes can be so diverse that it opens up a practical problem of management and access to the data generated. At the same time, ensuring the privacy, identity, confidentiality and security of students and their data is a “sine qua non” condition for complying with the legal issues involved while achieving the required ethical premises. This work proposes a modular and scalable data system architecture that solves the complexity of data management and access. On the one hand, it allows educational institutions to collect any data generated in both the teaching-learning and management processes. On the other hand, it will enable external access to this data under appropriate privacy and security conditions.

Background: Personal health data (PHD) research has been intensified over the last years, attracting the attention of scientists from different fields, such as software engineers, computer scientists and medical professionals. The increasing interest of researchers can be attributed to the exponential growth of the available PHD due to the widespread adoption of ubiquitous technology in everyday life, as well as to the potential of the ongoing digital transformation in healthcare. This increasing interest requires that academia has an overview of the published scientific literature to plan future endeavors.Objective: The main objective of this study is to identify and address research gaps in literature regarding PHD.Method: This paper conducts a systematic mapping study to summarize the existing PHD approaches in literature and to organize the selected studies according to six classification criteria: publication source, publication year, research types, empirical types, contribution types and research topic.Results: In total 79 papers have been included after fulfilling the inclusion criteria and have been classified accordingly. There is an increasing amount of attention that has been paid to PHD since 2014. The majority of papers is published in journals. The two main research types found were solution proposals and evaluation research. The majority of the selected papers were empirically evaluated. The main contribution types were methods and frameworks. Data privacy is the most frequently addressed topic in PHD literature, followed by data sharing.Conclusions: The findings of this systematic mapping study have implications for both researchers who are planning new studies in PHD and for practitioners who are working in connected health and would like to have an overview on the existent studies on PHD research area. [ABSTRACT FROM AUTHOR]

Background: Collaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy., Objective: We aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism., Methods: We developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets., Results: When one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values < 0.001)., Conclusion: By constructing a quorum to continue the modeling process, QuorumChain possesses the capability to tackle the situation of sites being unavailable. It inherits the capability of learning on network-of-networks, improves learning continuity, and provides data/software immutability, transparency, and provenance, which can be important in expediting clinical research., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

• We propose a concept called a designated cloud server-based multi-user certificateless authenticated encryption with conjunctive keyword search scheme (dmCLPAECKS). • In which the same document or e-mail is only encrypted once and can be retrieved by different recipients. The proposed scheme also supports conjunctive keyword search. • The proposed scheme does not allow an attacker to forge a valid ciphertext (i.e., dmCLPAECKS) sent by a data owner to a data receiver without knowing the data owner's private key; this is due to authenticated encryption. • In addition, without knowing the private key of the designated cloud server, an attacker would not perform a keyword search (i.e., dTest algorithm) even if the data receiver's trapdoor (i.e., dTrapdoor) was retrieved; this is due to a designated cloud server. • We define and analyze the ciphertext and trapdoor security models of the dmCLPAECKS scheme. • Security analysis shows that the proposed scheme can prevent inside keyword guessing attack (IKGA) in random oracle model. • Finally, we compare the dmCLPAECKS scheme with other related schemes based on the cost of computation and communication, and the comparison results describe that our proposed scheme achieves the lower communication and computation overhead. With the massive growth of the World Wide Web (i.e., internet), individual users or companies can store and manage large amounts of sensitive data to outsource it to the cloud server. Despite its advantages, such as reducing local storage, reducing overhead, and providing backups, it faces security and data privacy issues. Many public-key encryption with keyword search (PEKS) schemes are developed to resolve the above problems. Nevertheless, those are not efficiently suitable for multi-user scenario applications. Recently, a new scheme developed called Multi-user certificateless public key encryption with a conjunctive keyword search (mCLPECK) scheme. It contains two features: the multi-user environment and the conjunctive keyword search function. Nevertheless, it requires a secure channel while transmitting the trapdoor to the cloud server, which is not practical in applications, and it cannot resist the keyword guessing attack (KGA). We propose a primitive called a designated cloud server-based multi-user certificateless public key authenticated encryption with conjunctive keyword search (dmCLPAECKS) scheme. It doesn't require a secure channel between the designated cloud server and a data receiver and provides security against keyword guessing attack (KGA) in random oracle model. Furthermore, our proposed scheme shows better results than other related schemes in terms of communication and computation cost. [ABSTRACT FROM AUTHOR]

Abstract: Rapid advances in the networking technologies have prompted the emergence of the “software as service” model for enterprise computing, moreover, which is becoming one of the key industries quickly. “Database as service” model provides users power to store, modify and retrieve data from anywhere in the world, as long as they have access to the Internet, thus, being increasingly popular in current enterprise data management systems. However, this model introduces several challenges, an essential issue being how to implement SQL queries over encrypted data efficiently. To ensure data security, this model generally encrypts sensitive data at the trusted client’s site, before storing them into the non-trusted database service provider’s site, which, unfortunately, results in that SQL queries cannot be executed over the encrypted data immediately at the database service provider. In this paper we only focus on how to query encrypted character strings efficiently. Our strategy is that when storing character strings to the database service provider, we not only store the encrypted character strings themselves, but also generate some characteristic index values for these character strings, and store them in an additional field; and when querying the encrypted character strings, we first execute a coarse query over the characteristic index fields at the database service provider, in order to filter out most of tuples not related to the querying conditions, and then, we decrypt the rest tuples and execute a refined query over them again at the client site. In our strategy, we define an n-phase reachability matrix for a character string and use it as the characteristic index values, and based on such a definition, we present some theorems to split a SQL query into its server-side representation and client-side representation for partitioning the computation of a query across the client and the server and thus improving query performance. Finally, experimental results validate the functionality and effectiveness of our strategy. [Copyright &y& Elsevier]

A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance. [ABSTRACT FROM AUTHOR]