The privacy of personal information is aimed at protecting human rights both under the international human rights regime and the Saudi Arabian constitution and other statutes and regulations, subject only to some exceptions that include the protection of public health. The coronavirus disease 2019 (COVID-19) pandemic has brought about certain challenges that necessitate strategies to augment the conventional surveillance of infectious diseases, contact tracing, isolation, reporting and vaccination. Several governments institutions, and agencies presently adopt mobile applications for collecting, analyzing, managing, and sharing critical personal data of individuals infected with or exposed to COVID-19. While the benefits of sharing private information for achieving public health needs may not be disputed, the risk of breach of personal privacy is enormous. This had forced the national governments into a dilemma of either succumbing to public health needs, strictly respecting and protecting the privacy of individuals, or alternatively, balancing the two conflicting demands. There is a massive body of literature on the security and privacy of such mobile applications, but none has adequately explored and discussed public interest justifications under Saudi Arabian laws for alleged privacy breaches. We examined the health surveillance mobile app technologies currently in use in Saudi Arabia with the aim of determining the potential risks of data breaches under extant data protection laws. The paper recommends, among others, that any potential risk of breach to right to privacy of personal information under the law must be (justified by) the public health needs to protect society during the COVID-19 pandemic.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

While the notion of the smart city has grown in popularity, the backlash against smart urban infrastructure in the context of changing state-public relations has seldom been examined. This article draws on the case of Hong Kong’s smart lampposts to analyse the emergence of networked dissent against smart urban infrastructure during a period of unrest. Deriving insights from critical data studies, dissentworks theory, and relevant work on networked activism, the article illustrates how a smart urban infrastructure was turned into both a source and a target of popular dissent through digital mediation and politicisation. Drawing on an interpretive analysis of qualitative data collected from multiple digital platforms, the analysis explicates the citizen curation of socio-technic counter-imaginaries that constituted a consent of dissent in the digital realm, and the creation and diffusion of networked action repertoires in response to a changing political opportunity structure. In addition to explicating the words and deeds employed in this networked dissent, this article also discusses the technopolitical repercussions of this dissent for the city’s later attempts at data-based urban governance, which have unfolded at the intersections of urban techno-politics and local contentious politics. Moving beyond the common focus on neoliberal governmentality and its limits, this article reveals the underexplored pitfalls of smart urban infrastructure vis-à-vis the shifting socio-political landscape of Hong Kong, particularly in the digital age.

Massive open online courses (MOOCs) are intentionally designed to be easily accessible to many learners, regardless of their academic level or age. MOOCs leverage internet-based technology, allowing anybody with an internet connection to have unrestricted access, regardless of their location or time limitations. MOOCs provide a versatile and easy opportunity for acquiring top-notch education, enabling anyone to learn at their preferred speed, free from limitations of time, cost, or geographical location. Given the advantages they offer, MOOCs are a valuable method for improving the quality and availability of education in Indonesia. Following the outbreak of the COVID-19 pandemic, colleges and institutions have implemented the establishment of digital campuses. One important characteristic of these digital campuses is that they prioritize processes but overlook data and lack standardized standards. The problems and fundamental causes include challenges related to the comprehensive information architecture. The main factor contributing to this challenge is the absence of uniform and well-defined information standards. The existing connectivity and data exchange mechanisms in several schools are poor, leading to substantial data discrepancy among various departments due to the limited content of the fundamental data utilized. Moreover, the absence of clear information about the reliable source of data exacerbates the problem. The main objectives of data governance are to improve data quality, eliminate data inconsistencies, promote extensive data sharing, utilize data aggregation for competitive benefits, supervise data modifications based on data usage patterns, and comply with internal and external regulations and agreed-upon data usage standards. The aim of this project is to create a data governance framework that is customized to the specific conditions in Indonesia, with a specific emphasis on MOOC providers. The researcher chose design science research (DSR) as the research paradigm as it can successfully tackle relevant issues linked to the topic by creating innovative artefacts about the data governance framework for MOOC providers in Indonesia. This research highlights the necessity and significance of implementing a data governance framework for MOOC providers in Indonesia, hence increasing their awareness of this requirement. The researchers incorporated components from the data management body of knowledge (DMBOK) into their data governance framework. This framework includes ten components related to data governance, which are further divided into sub-components within the MOOC providers’ framework.