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Sharing data produced through health research projects has been increasingly recognized as a way to advance science more rapidly by facilitating discovery and increasing rigor and reproducibility. Much of the data collected from human subjects includes sufficient sociodemographic detail and/or covers sensitive topics and thus requires restricted data management and sharing practices. Over the last two decades, scientific organizations, presidential memoranda, and other sources have all called for increasing opportunities to share data. Recognizing the value of shared data, the National Institutes of Health (NIH) has issued a new Data Management and Sharing (DMS) Policy (Office of the Director, 2020), effective January 25, 2023. Prior to this updated policy, in 2009, the National Institute on Drug Abuse (NIDA) recognized the value of sharing data and established an archive, the National Addiction and HIV Data Archive Program (NAHDAP). This program focused on sharing data that was often highly sensitive generated from social and behavioral addiction research, including quantitative and qualitative assessments as well as biomarker and imaging data. NAHDAP has developed practices and curation standards to ensure datasets are improved and usable and provides technical assistance for both data depositors and users. We share three key lessons learned working to disseminate sensitive data over the last 13 years, including (1) protecting the confidentiality of human subjects, (2) ensuring careful consideration of costs for archiving data requiring human subject protections are managed, and (3) providing support to facilitate the discovery and use of the data. While this article will not provide a comprehensive review of these areas, we will provide major insights from our experiences while developing this archive program.

The evolution of technology research has yielded solutions that resolve the longstanding challenge of sharing sensitive medical data while ensuring data security and privacy. Blockchain-based technologies, particularly openDSU, play a pivotal role by enhancing transparency and security in transmitting confidential information. These innovations transcend traditional data-sharing barriers, achieving a harmonious balance between secure transmission and privacy. A noteworthy manifestation of this evolution is the PL project—a groundbreaking initiative that harnesses openDSU to develop a suite of decentralized applications (DApps). These DApps prove instrumental in simplifying the management of patient consent within clinical trials and facilitating the seamless exchange of medical data for diverse research endeavors. By streamlining consent processes and securely orchestrating data sharing, these applications underscore the potential of blockchain to revolutionize medical research methodologies. The integration of Internet of Things (IoT)-type devices further amplifies the transformative impact of these advancements. These interconnected devices bring forth an era of personalized medicine automation, where real-time patient data can be harnessed to inform tailored treatment strategies. This convergence heralds an era of precision medicine, where patient care is optimized through data-driven insights and interventions. A salient aspect of these novel solutions lies in their universal applicability. The systems and methodologies developed through the PL project can seamlessly integrate into any medical framework, offering a versatile blueprint for secure medical data sharing. The implications of this research are far-reaching, fostering collaborative research ecosystems, improving patient outcomes, and catalyzing advancements in medical science.

Despite Emails and websites being widely used for communication, collaboration, and day-to-day activity, not all online users have the same knowledge and skills when determining the credibility of visited websites and email content. As a result, phishing, an identity theft cyber-attack that targets humans rather than computers, was born to harvest internet users' confidential information by taking advantage of human behavior and hurting an organization's continuity, reputation, and credibility. Because the success of phishing attacks depends on human behavior, using the Health-Belief Model, the study's objective is to examine significant factors that influence online users' security behavior in the context of Email and website-based phishing attacks. The model included eight predictor variables and was validated using quantitative data from 138 academic staff. The study findings exhibit that 4 out of 8 predictor variables, namely Perceived-Barriers, Perceived-Susceptibility, Self-efficacy, and Security-Awareness, are statistically significant in determining users' security behavior. The study's outcome is to assist in the appropriate design of both online and offline content for cyber security awareness programs, focusing on Email and website-based phishing attacks.

Providing access to synthetic micro-data in place of confidential data to protect the privacy of participants is common practice. For the synthetic data to be useful for analysis, it is necessary that the density function of the synthetic data closely approximate the confidential data. Hence, accurately estimating the density function based on sample micro-data is important. Existing kernel-based, copula-based, and machine learning methods of joint density estimation may not be viable. Applying the multivariate moments’ problem to sample-based density estimation has long been considered impractical due to the computational complexity and intractability of optimal parameter selection of the density estimate when the true joint density function is unknown. This paper introduces a generalised form of the sample moment-based density estimate, which can be used to estimate joint density functions when only the information of empirical moments is available. We demonstrate optimal parametrisation of the moment-based density estimate based solely on sample data by employing a computational strategy for parameter selection. We compare the performance of the moment-based estimate to that of existing non-parametric and parametric density estimation methods. The results show that using empirical moments can provide a reasonable, robust non-parametric approximation of a joint density function that is comparable to existing non-parametric methods. We provide an example of synthetic data generation from the moment-based density estimate and show that the resulting synthetic data provides a reasonable disclosure-protected alternative for public release. [ABSTRACT FROM AUTHOR]

Despite Emails and websites being widely used for communication, collaboration, and day-to-day activity, not all online users have the same knowledge and skills when determining the credibility of visited websites and email content. As a result, phishing, an identity theft cyber-attack that targets humans rather than computers, was born to harvest internet users' confidential information by taking advantage of human behavior and hurting an organization's continuity, reputation, and credibility. Because the success of phishing attacks depends on human behavior, using the Health-Belief Model, the study's objective is to examine significant factors that influence online users' security behavior in the context of Email and website-based phishing attacks. The model included eight predictor variables and was validated using quantitative data from 138 academic staff. The study findings exhibit that 4 out of 8 predictor variables, namely Perceived-Barriers, Perceived-Susceptibility, Self-efficacy, and Security-Awareness, are statistically significant in determining users' security behavior. The study's outcome is to assist in the appropriate design of both online and offline content for cyber security awareness programs, focusing on Email and website-based phishing attacks. [ABSTRACT FROM AUTHOR]

Nowadays, in a networked society, the culture of security on social media platforms involves not only understanding confidentiality and controlling data, but also complying with these rules of confidentiality about what, why and to whom information is shared. Understanding privacy influences how confidential data is disclosed and defines user behavior on Facebook and Instagram. The paper focuses on what motivates users to provide access to confidential data and the effects of this decision. We will look at the generational differences and analyze the reasons for Z, Y, X and Boomers generations to post confidential data on social networks. The inductive thematic analysis, conducted on the basis of the semi-structured individual interview that we used as a method of data collection, will help to identify users' attitudes towards data security. The research identified that the attitude of users towards data security is influenced by the behavior, lifestyle of the age segment, but also by the benefits that come with the disclosure of confidential data. We were able to identify that users concerned about privacy and personal data are more reluctant and either do not provide personal data or provide it incompletely. The higher the level of privacy concerns, the less personal data users provide. In other words, the higher the level of security culture on social networks, the lower the degree of exposure to confidential data. This paper is relevant because it provides an x-ray of the behavior of Z, Y, X and Boomers generations, users of social networks, to identify the level of security culture on Facebook and Instagram platforms in relation to understanding privacy and the limits of the social contract.

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients' confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients' confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients' confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients' confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients' confidential data. In addition, the embedded patients' confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks. [ABSTRACT FROM AUTHOR]

Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable, it is still unclear how the notions of FAIR and Openness can be applied in the context of restricted data. Many methods have been proposed in support of the implementation of the principles, but there is yet no consensus among the scientific community as to the suitable mechanisms of making restricted data FAIR. We present here a systematic literature review to identify the methods applied by scientists when researching restricted data in a FAIR-compliant manner in the context of the FAIR principles. Through the employment of a descriptive and iterative study design, we aim to answer the following three questions: (1) What methods have been proposed to apply the FAIR principles to restricted data?, (2) How can the relevant aspects of the methods proposed be categorized?, (3) What is the maturity of the methods proposed in applying the FAIR principles to restricted data?. After analysis of the 40 included publications, we noticed that the methods found, reflect the stages of the Data Life Cycle, and can be divided into the following Classes: Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage and Post Data Usage. We observed that a large number of publications used ‘Access Control‘ and ‘Usage and License Terms’ methods, while others such as ‘Embargo on Data Release’ and the use of ‘Synthetic Data’ were used in fewer instances. In conclusion, we are presenting the first extensive literature review on the methods applied to confidential data in the context of FAIR, providing a comprehensive conceptual framework for future research on restricted access data.

One of the challenges in the Internet of Things systems is the security of the critical data, for example, data used for intrusion detection. The paper research construction of an intrusion detection system that ensures the confidentiality of critical data at a given level of intrusion detection accuracy. For this goal, federated learning is used to train an intrusion detection model. Federated learning is a computational model for distributed machine learning that allows different collaborating entities to train one global model without sharing data. This paper considers the case when entities have data that are different in attributes. Authors believe that it is a common situation for the critical systems constructed using Internet of Things (IoT) technology, when industrial objects are monitored by different sets of sensors. To evaluate the applicability of the federated learning for this case, the authors developed an approach and an architecture of the intrusion detection system for vertically partitioned data that consider the principles of federated learning and conducted the series of experiments. To model vertically partitioned data, the authors used the Secure Water Treatment (SWaT) data set that describes the functioning of the water treatment facility. The conducted experiments demonstrate that the accuracy of the intrusion detection model trained using federated learning is compared with the accuracy of the intrusion detection model trained using the centralized machine learning model. However, the computational efficiency of the learning and inference process is currently extremely low. It is explained by the application of homomorphic encryption for input data protection from different data owners or data sources. This defines the necessity to elaborate techniques for generating attributes that could model horizontally partitioned data even for the cases when the collaborating entities share datasets that differ in their attributes. [ABSTRACT FROM AUTHOR]

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients’ confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients’ confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients’ confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients’ confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients’ confidential data. In addition, the embedded patients’ confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.

One of the challenges in the Internet of Things systems is the security of the critical data, for example, data used for intrusion detection. The paper research construction of an intrusion detection system that ensures the confidentiality of critical data at a given level of intrusion detection accuracy. For this goal, federated learning is used to train an intrusion detection model. Federated learning is a computational model for distributed machine learning that allows different collaborating entities to train one global model without sharing data. This paper considers the case when entities have data that are different in attributes. Authors believe that it is a common situation for the critical systems constructed using Internet of Things (IoT) technology, when industrial objects are monitored by different sets of sensors. To evaluate the applicability of the federated learning for this case, the authors developed an approach and an architecture of the intrusion detection system for vertically partitioned data that consider the principles of federated learning and conducted the series of experiments. To model vertically partitioned data, the authors used the Secure Water Treatment (SWaT) data set that describes the functioning of the water treatment facility. The conducted experiments demonstrate that the accuracy of the intrusion detection model trained using federated learning is compared with the accuracy of the intrusion detection model trained using the centralized machine learning model. However, the computational efficiency of the learning and inference process is currently extremely low. It is explained by the application of homomorphic encryption for input data protection from different data owners or data sources. This defines the necessity to elaborate techniques for generating attributes that could model horizontally partitioned data even for the cases when the collaborating entities share datasets that differ in their attributes.

The data hiding method embeds the data into covers such as video, audio, and images, which are used for integrity authentication, media protection, communication covert, copyright protection etc. In this work, several reversible data hiding (RDH) algorithms are analysed. Here, RDH algorithms are classified into six categories. They are histogram shifting centred RDH, code division multiplexing‐based RDH, compression‐based RDH, contrast enhancement with RDH, and expansion‐based RDH, and RDH for encrypted images. In RDH,no information is lost when the message is recovered as the message is embedded into the host image. The embedded message is extracted using an extraction procedure. This study critically reviews the technological advancement for the past decade on the aspects of classification, pre‐processing, feature extraction, and analysis technique. The current trend in RDH is to insert confidential data into a video by using RDH in the encoded area that has a wide selection of applications on the confidential communication inside the area of cybersecurity. [ABSTRACT FROM AUTHOR]

Cyber security's importance is on the rise. Our world relies on technologies more than ever before. Government, military, organizations, financial institutions, universities and other businesses collect, process and store a large amount of information on computers. This trend is growing faster every day. As computers become a major source of information, they need to be protected. Every day a new type of cyber-attack came into an act which makes digital data more vulnerable day by day. Cyber security is the practice of ensuring the integrity, confidentiality, and availability (ICA) of information. It requires a broad variety of resources to secure networks, computers, services and data against threats or non-authorized entry, which encompasses best practices for risk control techniques and technologies. In this paper, we will address generations of cyber-attacks, attacker strategy, and biggest cyber -attacks in India. The statistical data of cyber-attacks and security will be discussed. The paper will also clarify emerging concepts of information technology and potential protection developments. In this article, intelligent protection strategies will be clarified to secure information from multiple intruders. [ABSTRACT FROM AUTHOR]

The BioMedIT project is funded by the Swiss government as an integral part of the Swiss Personalized Health Network (SPHN), aiming to provide researchers with access to a secure, powerful and versatile IT infrastructure for doing data-driven research on sensitive biomedical data while ensuring data privacy protection. The BioMedIT network gives researchers the ability to securely transfer, store, manage and process sensitive research data. The underlying BioMedIT nodes provide compute and storage capacity that can be used locally or through a federated environment. The network operates under a common Information Security Policy using state-of-the-art security techniques. It utilizes cloud computing, virtualization, compute accelerators (GPUs), big data storage as well as federation technologies to lower computational boundaries for researchers and to guarantee that sensitive data can be processed in a secure and lawful way. Building on existing expertise and research infrastructure at the partnering Swiss institutions, the BioMedIT network establishes a competitive Swiss private-cloud – a secure national infrastructure resource that can be used by researchers of Swiss universities, hospitals and other research institutions. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a domain wherein which the transfer of data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, the elliptic Galois cryptography protocol is introduced and discussed. In this protocol, a cryptography technique is used to encrypt confidential data that came from different medical sources. Next, a Matrix XOR encoding steganography technique is used to embed the encrypted data into a low complexity image. The proposed work also uses an optimization algorithm called Adaptive Firefly to optimize the selection of cover blocks within the image. Based on the results, various parameters are evaluated and compared with the existing techniques. Finally, the data that is hidden in the image is recovered and is then decrypted. [ABSTRACT FROM AUTHOR]

Sharing data produced through health research projects has been increasingly recognized as a way to advance science more rapidly by facilitating discovery and increasing rigor and reproducibility. Much of the data collected from human subjects includes sufficient sociodemographic detail and/or covers sensitive topics, and thus requires restricted data management and sharing practices. Over the last two decades, scientific organizations, presidential memoranda, and other sources have all called for increasing opportunities to share data. Recognizing the value of shared data, the National Institutes of Health issued a new Data Management and Sharing Policy, effective January 25, 2023. Prior to this updated policy, in 2009, the National Institute on Drug Abuse recognized the value of sharing data and established an archive, the National Addiction and HIV Data Archive Program. This program focused on sharing data, often highly sensitive, generated from social and behavioral addiction research, including quantitative and qualitative assessments as well as biomarker and imaging data. NAHDAP has developed practices and curation standards to ensure datasets are improved and usable, and provides technical assistance for both data depositors and users. We share three key lessons learned working to disseminate sensitive data over the last 13 years, including (1) protecting the confidentiality of human subjects; (2) ensuring careful consideration of costs for archiving data requiring protection ; and (3) providing support to facilitate the discovery and use of the data.

Disguising the presence of communication has become a severe concern in this highly digitalized world due to the unauthorized data access and network policy violations that are emerging rapidly. These issues have led to the application of cryptography technique as a mean for securing data by encrypting them. However, since the encrypted data can be seen by sophisticated intruders during the transmission, this may lead to its suspicion which can results in unauthorized access. Thereby, steganography is another technique for securing communication. Steganography is the practice of concealing confidential information in the codes that make up digital files. Different from encryption, however, steganography provides security by disguising the presence of communication. In this context, this paper presents an improved information hiding implemented based on difference expansion and modulus function. The previous method has only considered the image smooth areas where the difference value is 0 or 1 while ignoring other values for hiding data. These limitations may result in decreasing the embedding capacity for all images having few smooth areas. Hence, a new scheme that considers both positive and negative difference values to conceal secret data is developed. The experimental results prove that the proposed scheme achieves better results than the existing methods. [ABSTRACT FROM AUTHOR]

This paper studies information security for information communication technology (ICT) users in Russia. The study used dialectic, metaphysical and tabular methods. An analysis of the effectiveness of the legal regulation system of ICT security used in Russia starts with a presentation of the legal principles for ensuring the information security of those using ICT and the problem statement. Further, the paper analyzes federal laws; articles of the Russian Criminal Code related to the malicious use of ICT and the illegal receipt of personal information; the theoretical research in this area; the bill to provide consumers with the opportunity to use pre-installed Russian programs when selling certain types of technically complex goods; judicial practice, and the basic standards in this field. Legislative changes were studied for the period from 2015 to 2020. Using the example of these changes, the improvement of the field of ICT security in Russia was demonstrated. In conclusion, changes are proposed that will bring us closer to solving the problem of the emergence malicious ICT which collect user information, including confidential data. [ABSTRACT FROM AUTHOR]

Background: Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population., Objective: This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis., Methods: A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged., Results: The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly., Conclusions: With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine., (©Tobias Ueli Blatter, Harald Witte, Jules Fasquelle-Lopez, Jean Louis Raisaro, Alexander Benedikt Leichtle. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023.)

In the corporate world, a large number of people store their data on clouds. For doing this they need to give their confidential data in the hands of the third party, commonly known as service providers. These cloud service providers cannot be trusted since the complete data is stored in one single cloud. This increases security risks to the user’s sensitive data. Due to this issue of data integrity risk and service availability failure, the concept of “Cloud-of-Clouds” comes into picture. Cloud-of-clouds are also known as “inter-clouds” or “multi-clouds”. Use of cloud-ofclouds provides a higher level of security to the user’s confidential data. The aim of this paper is to secure the user’s data by using cloud-of clouds. [ABSTRACT FROM AUTHOR]

Oral archives collected by professional scholars and ordinary people interested in dialects and ethnology are a precious resource for various fields of study (from linguistics to anthropology, from economy to history and politics, etc.) and may contain documents that could be labeled as products of intangible cultural heritage, thus deserving safeguard. Grammo-foni. Le soffitte della voce (Gra.fo), a two-year project jointly conducted by Scuola Normale Superiore and the University of Siena (Regione Toscana PAR FAS 2007-13), discovered, digitized, cataloged and disseminated via a web portal nearly 3000 hours of speech recordings stemming from around 30 oral archives collected by scholars and amateurs in the Tuscan territory. Having preserved such a significant collection of oral documents (e.g. oral biographies, ethno-texts, linguistic questionnaires, oral literature), Gra.fo constitutes a precious repository of Tuscan memory and provides a first-hand documentation of Tuscan language varieties from the early 1960s to the present day [7], [9]. In this article, the Gra.fo project will be described in all its stages, which involve: fostering the level of awareness on the importance of preserving this valuable cultural heritage product; contacting the oral recordings’ owners and co-signing legal agreements for the temporary borrowing of the recordings and accompanying materials; collecting and digitizing the recordings and the accompanying materials; cataloging (with the self-developed software Audiografo) and partially transcribing the oral documents; implementing the downloadable online catalog http://grafo.sns.it/, an open-ended repository of oral texts which have hitherto been known to a very limited number of potential users. Some problematic issues related to the treatment of oral archives will also be discussed, together with the proposed solutions. These concern the carrier / document relation, the treatment of confidential information, and the cataloging of documents within other documents.

Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011−2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.

The evolution of technology research has yielded solutions that resolve the longstanding challenge of sharing sensitive medical data while ensuring data security and privacy. Blockchain-based technologies, particularly openDSU, play a pivotal role by enhancing transparency and security in transmitting confidential information. These innovations transcend traditional data-sharing barriers, achieving a harmonious balance between secure transmission and privacy. A noteworthy manifestation of this evolution is the PL project--a groundbreaking initiative that harnesses openDSU to develop a suite of decentralized applications (DApps). These DApps prove instrumental in simplifying the management of patient consent within clinical trials and facilitating the seamless exchange of medical data for diverse research endeavors. By streamlining consent processes and securely orchestrating data sharing, these applications underscore the potential of blockchain to revolutionize medical research methodologies. The integration of Internet of Things (IoT)-type devices further amplifies the transformative impact of these advancements. These interconnected devices bring forth an era of personalized medicine automation, where real-time patient data can be harnessed to inform tailored treatment strategies. This convergence heralds an era of precision medicine, where patient care is optimized through data-driven insights and interventions. A salient aspect of these novel solutions lies in their universal applicability. The systems and methodologies developed through the PL project can seamlessly integrate into any medical framework, offering a versatile blueprint for secure medical data sharing. The implications of this research are far-reaching, fostering collaborative research ecosystems, improving patient outcomes, and catalyzing advancements in medical science. [ABSTRACT FROM AUTHOR]