A framework for intelligent policy decision making based on a government data hub

Author ProofThe e-Oman Integration Platform is a data hub that enables data exchanges across government in response to transactions. With millions of transactions weekly, and thereby data exchanges, we propose to investigate the potential of gathering intelligence from these linked sources to help government officials make more informed decisions. A key feature of this data is the richness and accuracy, which increases the value of the learning outcome when augmented by other big and open data sources. We consider a high-level framework within a government context, taking into account issues related to the definition of public policies, data privacy, and the potential benefits to society. A preliminary, qualitative validation of the framework in the context of e-Oman is presented. This paper lays out foundational work into an ongoing research to implement government decision-making based on big data.“SmartEGOV: Harnessing EGOV for Smart Governance (Foundations, Methods, Tools)/NORTE-01-0145-FEDER-000037”, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (EFDR

The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach

Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. The introduction of a new CTA alone will not contain COVID-19. The best-case scenario for uptake requires between 90 and 95% of the entire population for containment. This does not factor in any loss due to people dropping out or device incompatibility or that only 79% of the population own a smartphone, with less than 40% in the over-65 age group. Hence, the best-case scenario is beyond that which could conceivably be achieved. We propose to build on some of the digital solutions already under development, with the addition of a Bayesian network model that predicts likelihood for infection supplemented by traditional symptom and contact tracing. When combined with freely available COVID-19 testing with results in 24 hours or less, an effective communication strategy and social distancing, this solution can have a very beneficial effect on containing the spread of this pandemic

Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing?

Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. However, very few groups have taken a holistic approach and presented a combined solution. None has presented their CTA in such a way as to ensure that even the most suggestible member of our community does not become complacent and assume that CTA operates as an invisible shield, making us and our families impenetrable or immune to the disease. We propose to build on some of the digital solutions already under development that, with addition of a Bayesian model that predicts likelihood for infection supplemented by traditional symptom and contact tracing, that can enable us to reach 90% of a population. When combined with an effective communication strategy and social distancing, we believe solutions like the one proposed here can have a very beneficial effect on containing the spread of this pandemic

Causas e problemas de privacidade de dados em sistemas de big data analytics: uma revisão sistemática da literatura

This study aims to identify and describe data privacy issues and their causes based on the literature on the subject. This is an exploratory research based on a systematic literature review. It explores scientific databases and serves as a basis for future descriptive and explanatory research, which will allow the improvement of the information systems, adapting them to the privacy needs of citizens. Nine data privacy issues and seven causes were identified

Analyzing Small Businesses\u27 Adoption of Big Data Security Analytics

Despite the increased cost of data breaches due to advanced, persistent threats from malicious sources, the adoption of big data security analytics among U.S. small businesses has been slow. Anchored in a diffusion of innovation theory, the purpose of this correlational study was to examine ways to increase the adoption of big data security analytics among small businesses in the United States by examining the relationship between small business leaders\u27 perceptions of big data security analytics and their adoption. The research questions were developed to determine how to increase the adoption of big data security analytics, which can be measured as a function of the user\u27s perceived attributes of innovation represented by the independent variables: relative advantage, compatibility, complexity, observability, and trialability. The study included a cross-sectional survey distributed online to a convenience sample of 165 small businesses. Pearson correlations and multiple linear regression were used to statistically understand relationships between variables. There were no significant positive correlations between relative advantage, compatibility, and the dependent variable adoption; however, there were significant negative correlations between complexity, trialability, and the adoption. There was also a significant positive correlation between observability and the adoption. The implications for positive social change include an increase in knowledge, skill sets, and jobs for employees and increased confidentiality, integrity, and availability of systems and data for small businesses. Social benefits include improved decision making for small businesses and increased secure transactions between systems by detecting and eliminating advanced, persistent threats

Strategies to Implement Big Data Analytics in Telecommunications Organizations

Information Technology (IT) leaders who do not invest in big data projects may struggle to gain a competitive advantage and business insights to improve performance. Grounded in the Kotterâs change and Six Sigma models, the purpose of this qualitative multiple case study was to explore strategies IT leaders used to implement big data analytics successfully. The participants comprised 4 IT leaders from 2 telecommunication organizations in the United States of America, who effectively used strategies to promote and maximize competitive advantage using big data analytics. Data were collected from semistructured interviews, company documents, and project-related documents and were analyzed using thematic analysis. Four themes emerged: communication, training, employee involvement in decisions, and teamwork strategy. A key recommendation emerging from these findings is for IT leaders to use successful communication strategies to communicate the vision and objectives effectively to all the different levels within the organization. The successful communication of strategy can help with the analysis of business trends and forecasts and improve overall organizational performance and competitive advantage. The implications for positive social change include the potential for job creation, thus catalyzing economic growth within communities

RESPECTING THE ETHICAL TENSION BETWEEN SURVEILLANCE AND PRIVACY IN PROMOTING PUBLIC HEALTH AND DISEASE MANAGEMENT

The recognition of the need to undertake surveillance and to protect privacy is well established. However, the continually changing circumstances and fast-paced development of healthcare today requires a continuing need to respect this ethical tension between surveillance and privacy. Hence, this dissertation is to respect the ethical tension between surveillance and privacy in promoting public health and disease management. This dissertation investigates the ethics of conducting public health surveillance, including the challenges associated with obtaining consent and protecting data from unauthorized access. The dissertation will focus on the ethical consequences of big data, including issues associated with obtaining informed consent, data ownership, and privacy. As the dissertation concludes, it will provide an ethical justification of observing privacy in public health surveillance. The analysis is pursued in the dissertation in the following manner. After a brief introduction in Chapter 1, the analysis begins in Chapter 2 by explaining the importance of consent with regard to protecting privacy, including confidentiality in clinical ethics. Chapter 3 moves the discussion to the realm of public health ethics, discussing two examples of population health matters to illustrate the dissertation’s focus. Chapter 4 focuses on the complex issue of disease management for which the ethical tension between surveillance and privacy is pivotal. Chapter 5 then discusses the critical need for respecting this ethical tension in research protocols from a global perspective. Chapter 6 moves the discussion to the fast-developing debate of data analysis in healthcare for which respecting the ethical tension between surveillance and privacy will be pivotal for the continuing success in this new arena. Finally, Chapter 7 provides a brief conclusion to the dissertation