Statistical Disclosure Control for Microdata Using the R-Package sdcMicro

Efficient data management in a large-scale epidemiology research project

eHR is one of the main development area in healthcare sector to ensure a high quality and effective healthcare service in Hong Kong is provided. However, the present development of eHR in Hong Kong is mainly focused on public sectors healthcare providers – the hospitals and clinics under HA and DH. Most of the private hospitals and clinics are still using paper based health records. Although some of them may have implemented their own eHR systems, there is no interconnection among other healthcare providers.
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\nIn this dissertation, the eHR system development in Hong Kong for both public and private sectors will be reviewed, to figure out the development of eHR and various clinical management systems, as well as the problems facing by the healthcare workers and patients.
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\nAlso, HKSAR government shows supportive to the eHR development both in the governance and financial aspects. To facilitate the coordination of developing her sharing system among different healthcare providers, an eHR Office has been setup under Food and Health Bureau for this purpose. The eHR office will monitor the progress of the eHR development process. As HA has a well-developed world-known Clinical Management System (CMS), which handles patient records in electronic forms in public hospitals daily. HA acts as one major advisor in her implementation for HKSAR.
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\nData privacy and data security issues are the major concerns of healthcare workers and patients. The Personal Data Protection Ordinance (PDPO) provides protection on the data privacy in legal aspect. However, no legislation on data privacy has been specified for eHR currently. Meanwhile, various physical security protections have been adopted in the implementation of eHR in technology side, which provided a certain level of data security to the system.
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\nChinese Medicine has been developed rapidly recently, it is expected the Chinese Medicine would become one of the core service area in healthcare sector in Hong Kong, sharing the healthcare service with the Western Medicine. However, there is no integration between Chinese Medicine and Western Medicine in current her sharing system development.
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\neHR development involves huge investment, to evaluate the feasibility of developing the eHR system, a scientific tool is recommended, a Cost and Benefit Analysis is hence conducted for the eHR in Hong Kong, to compare the effectiveness of eHR with the traditional paper-based health records in the healthcare setting. As recommended from the CBA, the eHR system will be developed with the consideration on the system flexibility and the adaptability from all the healthcare providers. On the other hand, the implementation of the her system will be a long and complex process and will require the contribution and participation from all parties.

After several years of steady progress, the Generative artificial intelligence (AI) and large language models (LLMs, their applications to text) fields have accelerated tremendously since the end of 2022 and the public launch of ChatGPT. This is due to record-breaking model sizes and performances in the last couple of months, triggering unprecedented adoption curves from end users across the world. Even though regulators reacted fast, sharing their first recommendations, auditing emerging players, amending their AI regulation drafts or launching dedicated working groups, these efforts will require several months or years to come to fruition. There are multiple reasons for this. LLMs are complex technological objects made of gigantic foundational models trained on enormous quantities of texts, coupled with dedicated interfaces and action agents. They present a huge potential to perform high varieties of tasks with very high quality but also important risks in terms of costs, content accuracy, transparency, data privacy, security and ethics. Finally, the current ecosystem of stakeholders is very dynamic but also immature. In this uncertain context, the digital banking industry has been reacting ambivalently, with major players banning employee access to ChatGPT and publicly communicating on new LLM initiatives at the same time. This can be explained by the huge potential offered by these technologies to transform their business, coupled with many open questions in terms of technological set-up, usage, compliance and profitability. As these technologies seem to be too transformative for the industry incumbents to just wait and see, they should start creating the right conditions to learn how to use them, by identifying relevant use cases, choosing adapted and simple solutions, designing relevant user experiences, building the right teams, environment, data sets and operating model, and actively engaging in regulatory conversations.

BackgroundThe limited availability of maternal and child health data has limited progress in reducing mortality and morbidity among pregnant women and children. Global health agencies, leaders, and funders are prioritizing strategies that focus on acquiring high quality health data. Electronic maternal and child health registries (eRegistries) offer a systematic data collection and management approach that can serve as an entry point for preventive, curative and promotive health services. Due to the highly sensitive nature of reproductive health information, careful consideration must be accorded to privacy, access, and data security. In the third paper of the eRegistries Series, we report on the current landscape of ethical and legal governance for maternal and child health registries in developing countries.MethodsThis research utilizes findings from two web-based surveys, completed in 2015 that targeted public health officials and health care providers in 76 countries with high global maternal and child mortality burden. A sample of 298 public health officials from 64 countries and 490 health care providers from 59 countries completed the online survey. Based on formative research in the development of the eRegistries Governance Guidance Toolkit, the surveys were designed to investigate topics related to maternal and child health registries including ethical and legal issues.ResultsAccording to survey respondents, the prevailing legal landscape is characterized by inadequate data security safeguards and weak support for core privacy principles. Respondents from the majority of countries indicated that health information from medical records is typically protected by legislation although legislation dealing specifically or comprehensively with data privacy may not be in place. Health care provider trust in the privacy of health data at their own facilities is associated with the presence of security safeguards.ConclusionAddressing legal requirements and ensuring that privacy and data security of women’s and children’s health information is protected is an ethical responsibility that must not be ignored or postponed, particularly where the need is greatest. Not only are the potential harm and unintended consequences of inaction serious for individuals, but they could impact public trust in health registries leading to decreased participation and compromised data integrity.

The 5G wireless networks are potentially revolutionizing future technologies. The 5G technologies are expected to foresee demands of diverse vertical applications with diverse requirements including high traffic volume, massive connectivity, high quality of service, and low latency. To fulfill such requirements in 5G and beyond, new emerging technologies such as SDN, NFV, MEC, and CC are being deployed. However, these technologies raise several issues regarding transparency, decentralization, and reliability. Furthermore, 5G networks are expected to connect many heterogeneous devices and machines that will raise several security concerns regarding users’ confidentiality, data privacy, and trustworthiness. To work seamlessly and securely in such scenarios, future 5G networks need to deploy smarter and more efficient security functions. Motivated by the aforementioned issues, blockchain was proposed by researchers to overcome 5G issues because of its capacities to ensure transparency, data reliability, trustworthiness, immutability in a distributed environment. Indeed, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized technologies. In this chapter, we discuss the integration of the blockchain with 5G networks and beyond. We then present how blockchain applications in 5G networks and beyond could facilitate enabling various services at the edge and the core.

The integration of generative AI has revolutionized marketing strategies across various industries, including luxury brands. While AI presents opportunities for innovation, luxury brands face the unique challenge of using this technology without compromising their core identityexclusivity, heritage, and craftsmanship. This research explores the impact of AI on luxury branding, highlighting its role in enhancing personalization, optimizing customer experience, and redefining exclusivity in the digital age. Luxury brands have historically maintained their prestige through limited availability, high quality craftsmanship, and deep-rooted heritage. However, the shift towards digital platforms, accelerated by e-commerce growth and changing consumer preferences, has forced these brands to adapt. Younger generations, particularly Gen Z, expect digital experiences without losing the prestige of traditional luxury retail. Generative AI offers a solution by enabling personalized client interactions, AI-powered styling consultations, and even digital co-creation of exclusive products. Despite its benefits, AI in luxury branding also presents ethical challenges, including concerns over data privacy and the potential dilution of brand authenticity. This study emphasizes the importance of a balanced approach, ensuring AI enhances the luxury experience rather than replacing the personal touch that defines it.

The widespread use of smart devices, such as phones and live-streaming cameras, has ushered in an era where digital images can be captured and shared on social networks anytime and anywhere. Sharing images demands more bandwidth and stricter security than text. This prevalence poses challenges for secure image forwarding, as it is susceptible to privacy leaks when sharing data. While standard encryption algorithms can safeguard the privacy of textual data, image data entail larger volumes and significant redundancy. The limited computing power of smart devices complicates the encrypted transmission of images, creating substantial obstacles to implementing security policies on low-computing devices. To address privacy concerns regarding image sharing on social networks, we propose a lightweight data forwarding mechanism for resource-constrained environments. By integrating large-scale data forwarding with visual cryptography, we enhance data security and resource utilization while minimizing overhead. We introduce a downsampling-based non-expansive scheme to reduce pixel expansion and decrease encrypted image size without compromising decryption quality. Experimental results demonstrate that our method achieves a peak signal-to-noise ratio of up to 20.54 dB, and a structural similarity index of 0.72, outperforming existing methods such as random-grid. Our approach prevents size expansion while maintaining high decryption quality, addressing access control gaps, and enabling secure and efficient data exchange between interconnected systems.

RIO GRANDE VALLEY, the Robert C. Vackar College of Business and Entrepreneurship, School of Accountancy invites applications from

The huge availability and prosperity of net technology results in raised on-line media sharing over the cloud platform which has become one of the important resources and tools for development in our societies. So, in the epoch of enormous data great amount of sensitive information and transmission of different media transmitted over the net for communication. And recently, fog computing has captured the world's attention due to their inherent features relevant compared to the cloud domain, but this push to head for many issues related to data security and privacy in fog computing which it's still under studied in their initial juncture. Therefore, in this paper, we will review a security system that relies on encryption as a kind of effective solution to secure image data. We use an approach of using chaotic map plus space curve techniques moreover the confusion and diffusion strategies are carried out utilizing Hilbert curvature and chaotic map such as two-dimensional Henon map (2D-HM) to assert image confusion with pixel level permutation. Also, we relied in our system the way of shuffling the image with blocks and use a key for each block, which is chooses randomly to have a high degree of security. The efficiency of the proposed technique has been tested utilizing different investigations like analysis of entropy [7.9993], NPCR [99.6908%] and finally UACI [33.6247%]. Analysis of results revealed that the proposed system of image encryption technique has favorable effects, and can achieve a good result moreover it fights different attacks and by comparing with another techniques denote that our proposed fulfills high security level with high quality.

The increase of data leaks, attacks, and other ransom-ware in the last few years have pointed out concerns about data security and privacy. All this has negatively affected the sharing and publication of data. To address these many limitations, innovative techniques are needed for protecting data. Especially, when used in machine learning based-data models. In this context, differential privacy is one of the most effective approaches to preserve privacy. However, the scope of differential privacy applications is very limited (e. g. numerical and structured data). Therefore, in this study, we aim to investigate the behavior of differential privacy applied to textual data and time series. The proposed approach was evaluated by comparing two Principal Component Analysis based differential privacy algorithms. The effectiveness was demonstrated through the application of three machine learning models to both anonymized and primary data. Their performances were thoroughly evaluated in terms of confidentiality, utility, scalability, and computational efficiency. The PPCA method provides a high anonymization quality at the expense of a high time-consuming, while the DPCA method preserves more utility and faster time computing. We show the possibility to combine a neural network text representation approach with differential privacy methods. We also highlighted that it is well within reach to anonymize real-world measurements data from satellites sensors for an anomaly detection task. We believe that our study will significantly motivate the use of differential privacy techniques, which can lead to more data sharing and privacy preserving.

BackgroundRandomised implementation trials evaluate the effects of implementation strategies on implementation outcomes and may also monitor clinical effectiveness. Routine healthcare data are used in implementation trials for participant identification, intervention delivery, and/or outcome ascertainment. Trial efficiency (scientific, operational, statistical, and economic) is operationalised across trial design, processes, superstructure, infrastructure, and stakeholder engagement (the Trial Efficiency Pentagon). Despite frequent usage, the contribution of routine data to implementation trial efficiency remains underexplored. We aimed to investigate how the use of routine healthcare data affects trial efficiency in two implementation trials.MethodsWe conducted a qualitative comparative case study of two implementation trials, one UK-based and one US-based. Participants were purposively sampled from trial teams involved in the use and management of routine healthcare data. Data were collected through semi-structured interviews, document analysis, and feedback workshops. Framework analysis guided by the Trial Efficiency Pentagon was used to analyse the data, and data flow diagrams were developed to visualise routine data pathways within each trial.ResultsThe two trials (DIGITS and IMP2ART) used routine data to characterise the practice population of eligible patients, support clinical and economic outcome evaluation, facilitate audit and feedback, and assist in intervention delivery. Common facilitators that supported the use of routine data included sufficient IT and hardware capacity, relatively low cost, centralised regulatory approval for multi-site studies, and strong collaboration and partnerships. Common barriers included administrative complexity, redundant bureaucratic processes, and challenges with data sharing requirements. Key differences included the DIGITS trial’s in-house data warehouses within an integrated healthcare system ensured high data quality and enabled preliminary analyses. In contrast, the IMP2ART trial, managing a larger national sample, employed an external research database to integrate data from various EHR systems but faced challenges such as legacy systems, diverse coding practices and site-specific approvals. Data quality can act as either a facilitator or a barrier.ConclusionsRoutine data has an impact on implementation trial efficiency across trial design, processes, superstructure, infrastructure, and stakeholder engagement. To improve trial efficiency in public healthcare systems, researchers must address technological and regulatory barriers to accessing data. In private healthcare systems, data use and access hinges on investing in robust IT infrastructure and ensuring comprehensive organisational commitment.Trial registrationIMP2ART trial registration: ISRCTN15448074; DIGITS trial Clinicaltrials.gov Identifier: NCT05160233.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13063-026-09706-3.

In recent years, advanced machine learning techniques have demonstrated remarkable achievements in many areas. Despite the great success, one of the bottlenecks in applying machine learning techniques in real world applications lies in the lack of a large amount of high-quality training data from diverse domains. Meanwhile, massive personal data is being generated by mobile devices and is often underutilized. To bridge the gap, we propose a general dataset purchasing framework, named CrowdBuy and CrowdBuy++, based on crowdsourcing, with which a buyer can efficiently buy desired data from available mobile users with quality guarantee in a way respecting users' data ownership and privacy. We present a complete set of tools including privacy-preserving image dataset quality measurements and image selection mechanisms, which are budget feasible, truthful and highly efficient for mobile users. We conducted extensive evaluations of our framework on large-scale images and demonstrate that the system is capable of crowdsourcing high quality datasets while preserving image privacy with little computation and communication overhead.

BackgroundTelehealth is a great approach for providing high quality health care services to people who cannot easily access these services in person. However, because of frequently reported health data breaches, many people may hesitate to use telehealth-based health care services. It is necessary for telehealth care providers to demonstrate that they have taken sufficient actions to protect their patients’ data security and privacy. The government provided a HIPAA audit protocol that is highly useful for internal security and privacy auditing on health care systems, however, this protocol includes extensive details that are not always specific to telehealth and therefore is difficult to be used by telehealth practitioners.ObjectiveThe goal of this study was to develop and validate a telehealth privacy and security self-assessment questionnaire for telehealth providers.MethodsIn our previous work, we performed a systematic review on the security and privacy protection offered in various telehealth systems. The results from this systematic review and the HIPAA audit protocol were used to guide the development of the self-assessment questionnaire. The draft of the questionnaire was created by the research team and distributed to a group of telehealth providers for evaluating the relevance and clarity of each statement in the draft. The questionnaire was adjusted and finalized according to the collected feedback and face-to-face discussions by the research team. A website was created to distribute the questionnaire and manage the answers from study participants. A psychometric analysis was performed to evaluate the reliability of the questionnaire.ResultsThere were 84 statements in the draft questionnaire. Five telehealth providers provided their feedback to the statements in this draft. They indicated that a number of these statements were either redundant or beyond the capacity of telehealth care practitioners, who typically do not have formal training in information security. They also pointed out that the wording of some statements needed to be adjusted. The final released version of the questionnaire had 49 statements. In total, 31 telehealth providers across the nation participated in the study by answering all the statements in this questionnaire. The psychometric analysis indicated that the reliability of this questionnaire was high.ConclusionWith the availability of this self-assessment questionnaire, telehealth providers can perform a quick self-assessment on their telehealth systems. The assessment results may be used to identify possible vulnerabilities in telehealth systems and practice or demonstrate to patients the sufficient security and privacy protection to patients’ data.

SummaryOffshore fields present a growing need to guarantee safety and productivity while minimizing operational costs and increasing remote assistance. Brownfields are more exposed to risks due to the presence of aged assets requiring in depth inspections to assess potential life extensions. This challenge was tackled with a comprehensive approach to asset integrity management based on the enhanced use of digital solutions to enable new health care services on offshore assets, like CALM Buoys.In line with the recent Oil & Gas industry trends, new digital technologies have been recently developed and deployed on board our fleet of CALM (Catenary Anchor Leg Mooring) Buoys, such as the 3C Telemetry system, Inspection Tablets, the IDEA Web Portal and the Marine Drone. All these new digital solutions will be presented in the proposed paper concerning their technical capabilities and the overall integrity performance improvements achieved with their enhanced use on offshore assets.The 3C Telemetry system converts and upgrades CALM Buoys into smart, internet-friendly offloading terminals, connecting the system to Cloud services and ensuring secured data transmission, treatment, storage, and privacy, while delivering reliable accurate information to operators anywhere in the world. Inspection tablets are used to optimize health check campaigns on Buoys with a real-time and remote back office engineering support. These systems can also be connected to the IDEA (Imodco Digital Experience Access) Web Portal to allow online data visualization and analysis of the mooring systems performance."The Marine Drone is an unmanned survey vehicle to perform diverless UWILD (Underwater Inspection in Lieu of Dry-docking). The system can perform in depth visual inspections with its ROV (Remotely Operated Vehicle) and high-resolution subsea layout mapping of CALM buoys’ structures with its 3D bathymetry system, all providing high quality digital data post processed by advanced analytical tools for integrity analysis and preventive maintenance planning" (Castro, R., et al. 2020).Data management has become the most valuable asset for companies seeking to have a better understanding and to continuously improve operations. This paper will demonstrate how Buoys and passive (process wise) equipment, like Turrets, can be operated in new ways: 1. Connected Asset (IoT): 3C Telemetry, Tablets, and the Marine Drone. 2. Platform to share/connect data to algorithms/users: IDEA System. 3. New operating business models enabled by health care approach.

The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream tasks like cancer diagnosis. Furthermore, different stainings lead to biases in the training which in case of domain shifts negatively affect the test performance. Therefore, in this paper we propose MultiStain-CycleGAN, a multi-domain approach to stain normalization based on CycleGAN. Our modifications to CycleGAN allow us to normalize images of different origins without retraining or using different models. We perform an extensive evaluation of our method using various metrics and compare it to commonly used methods that are multi-domain capable. First, we evaluate how well our method fools a domain classifier that tries to assign a medical center to an image. Then, we test our normalization on the tumor classification performance of a downstream classifier. Furthermore, we evaluate the image quality of the normalized images using the Structural similarity index and the ability to reduce the domain shift using the Fréchet inception distance. We show that our method proves to be multi-domain capable, provides a very high image quality among the compared methods, and can most reliably fool the domain classifier while keeping the tumor classifier performance high. By reducing the domain influence, biases in the data can be removed on the one hand and the origin of the whole slide image can be disguised on the other, thus enhancing patient data privacy.