Citizen empowerment through smart surveillance: evidence from Indian smart cities

PurposeCitizens often perceive surveillance by government authorities as oppressive and, hence, demonstrate reluctance in value co-creation from such services. This study aims to investigate the challenges and benefits of citizen empowerment through technology-driven surveillance or "smart surveillance.”Design/methodology/approachGuided by Dynamic Capability theory, the authors conduct in-depth interviews with officers in-charge of surveillance in smart cities. Given the contemporary advancements, this approach allows a retrospective and real-time understanding of interviewees' experiences with smart surveillance.FindingsThe authors develop five propositions for citizen empowerment through smart surveillance to summarize the findings of this study.Research limitations/implicationsThis study advances the relevance of Dynamic Capability in public administration.Practical implicationsSmart city authorities and policymakers may leverage the insights provided in this study to design appropriate policies for smart surveillance.Originality/valueThe authors find that factors such as digital technology and infrastructure, information management, skill divide and perceived return on investment may influence citizen empowerment through smart surveillance.

Managing Data Protection and Privacy on Cloud

When pandemic rose in 2020, people were fighting against COVID-19 virus and organizations had accelerated their digitization and cloud adoption rapidly (De et al. in Int J Inf Manag 55:102171, 2020 [1]) to meet the online based business during the lockdown. This chaos helped fraudsters and attackers taking advantage of the momentary lack of security controls and oversight. Federal Investigation Bureau (FBI) Internet Crime Compliant Center (IC3) 2020 reported highest number of complaints in 2020 (791 k + ) compared to prior five years (298 k + in 2016), with peak losses reported ($4.2 Billion in 2020 compared to $1.5 Billion in 2016) (Internet Crime Complaint Center in Internet crime report. Federal Bureau of Investigation, Washington, D.C., 2020 [2]). Majority of these incidents were connected to financial fraud, identity fraud, and phishing for personally identifiable information (PII). Considering the severity and impact of personal data exposure over cloud and hybrid environment, this paper provides a brief overview of prior research and discuss technical solutions to protect data across heterogeneous environments and ensure privacy regulations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Responding to the coronavirus disease-2019 pandemic with innovative data use: The role of data challenges

Innovative, responsible data use is a critical need in the global response to the coronavirus disease-2019 (COVID-19) pandemic. Yet potentially impactful data are often unavailable to those who could utilize it, particularly in data-poor settings, posing a serious barrier to effective pandemic mitigation. Data challenges, a public call-to-action for innovative data use projects, can identify and address these specific barriers. To understand gaps and progress relevant to effective data use in this context, this study thematically analyses three sets of qualitative data focused on/based in low/middle-income countries: (a) a survey of innovators responding to a data challenge, (b) a survey of organizers of data challenges, and (c) a focus group discussion with professionals using COVID-19 data for evidence-based decision-making. Data quality and accessibility and human resources/institutional capacity were frequently reported limitations to effective data use among innovators. New fit-for-purpose tools and the expansion of partnerships were the most frequently noted areas of progress. Discussion participants identified building capacity for external/national actors to understand the needs of local communities can address a lack of partnerships while de-siloing information. A synthesis of themes demonstrated that gaps, progress, and needs commonly identified by these groups are relevant beyond COVID-19, highlighting the importance of a healthy data ecosystem to address emerging threats. This is supported by data holders prioritizing the availability and accessibility of their data without causing harm;funders and policymakers committed to integrating innovations with existing physical, data, and policy infrastructure;and innovators designing sustainable, multi-use solutions based on principles of good data governance.

The digital traveller: implications for data ethics and data governance in tourism and hospitality

Purpose>Big data and analytics are being increasingly used by tourism and hospitality organisations (THOs) to provide insights and to inform critical business decisions. Particularly in times of crisis and uncertainty data analytics supports THOs to acquire the knowledge needed to ensure business continuity and the rebuild of tourism and hospitality sectors. Despite being recognised as an important source of value creation, big data and digital technologies raise ethical, privacy and security concerns. This paper aims to suggest a framework for ethical data management in tourism and hospitality designed to facilitate and promote effective data governance practices.Design/methodology/approach>The paper adopts an organisational and stakeholder perspective through a scoping review of the literature to provide an overview of an under-researched topic and to guide further research in data ethics and data governance.Findings>The proposed framework integrates an ethical-based approach which expands beyond mere compliance with privacy and protection laws, to include other critical facets regarding privacy and ethics, an equitable exchange of travellers' data and THOs ability to demonstrate a social license to operate by building trusting relationships with stakeholders.Originality/value>This study represents one of the first studies to consider the development of an ethical data framework for THOs, as a platform for further refinements in future conceptual and empirical research of such data governance frameworks. It contributes to the advancement of the body of knowledge in data ethics and data governance in tourism and hospitality and other industries and it is also beneficial to practitioners, as organisations may use it as a guide in data governance practices.

Social data governance: From reflective practices to comparative synthesis

This special theme brings together reflections and deliberations regarding the design, implementation, and development of data governance. By addressing “social data governance” as the keyword of the special theme, we aim to further the discussion on a contextual understanding of both the governing foundations and effects of data, dataism, and datafication in different societies. Such a discussion reminds us to pay particular attention to—and thus account for—the social dynamics that underpin and contextualize the design, operation, and promotion of quantified governing mechanisms in which information on social behaviors is collected, datafied, manipulated, and represented. Essentially, the social dynamics of data governance have existed for a long time and in many forms, ranging from credit bureaus’ scrutiny, evaluation, and labeling of their customers to internet-enabled massive data collection and scoring systems used by governments, and to automated contact tracing techniques as a centerpiece of dataveillance and infection control amid the COVID-19 pandemic. Nevertheless, scholarly work from a wide range of disciplines like law, mathematics, and business and with diverse geographical foci has not yet been comparatively and reflectively articulated. Being rich and diverse, the special theme advances such a requisite understanding of the status and relevance of social dynamics of data governance mechanisms based on a wide range of empirical cases around the globe. To scrutinize the social dynamics helps illuminate and contrast divergent manifestations of data governance and their underlying mechanisms. © The Author(s) 2022.

Navigating data governance approvals to use routine health and social care data to evidence the hidden population with severe obesity: a case study from a clinical academic's perspective.

Background: Front-line professionals are uniquely placed to identify evidence gaps and the way routinely-collected data can help address them. This knowledge can enable incisive, clinically-relevant research. Aim: To document an example of the real-world approvals journey within the current NHS/Higher Education regulatory landscape, from the perspective of an experienced nurse undertaking doctoral study as a clinical academic. Methods: An instrumental case-study approach is used to explore the approvals process for a mixed-methods study. Relevant context is highlighted to aid understanding, including introduction of the General Data Protection Regulation and the integration of health and social care services. Results: Formal approvals by nine separate stakeholders from four different organisations took nearly 3 years, including 15 initial or revised applications, assessments or agreements. Obstacles included: conflicting views on what constitutes 'research' or 'service evaluation'; isolated decision-making; fragmented data systems; multiple data controllers and a changing data governance environment. The dual perspectives of being both clinician and academic using routine data are explored. Conclusions: Practitioners face a complex approvals process to use data they routinely collect, for research or evaluation purposes. Use of data during the COVID-19 pandemic has demonstrated the need for streamlining of data governance processes. Practical recommendations are outlined.

A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study.

BACKGROUND: Disease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards. OBJECTIVE: We sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources. METHODS: Following a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems-a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds. RESULTS: Data from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability. CONCLUSIONS: We described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

Social data governance: Towards a definition and model

With the surge in the number of data and datafied governance initiatives, arrangements, and practices across the globe, understanding various types of such initiatives, arrangements, and their structural causes has become a daunting task for scholars, policy makers, and the public. This complexity additionally generates substantial difficulties in considering different data(fied) governances commensurable with each other. To advance the discussion, this study argues that existing scholarship is inclined to embrace an organization-centric perspective that primarily concerns factors and dynamics regarding data and datafication at the organizational level at the expense of macro-level social, political, and cultural factors of both data and governance. To explicate the macro, societal dimension of data governance, this study then suggests the term "social data governance" to bring forth the consideration that data governance not only reflects the society from which it emerges but also (re)produces the policies and practices of the society in question. Drawing on theories of political science and public management, a model of social data governance is proposed to elucidate the ideological and conceptual groundings of various modes of governance from a comparative perspective. This preliminary model, consisting of a two-dimensional continuum, state intervention and societal autonomy for the one, and national cultures for the other, accounts for variations in social data governance across societies as a complementary way of conceptualizing and categorizing data governance beyond the European standpoint. Finally, we conduct an extreme case study of governing digital contact-tracing techniques during the pandemic to exemplify the explanatory power of the proposed model of social data governance.

Factors Affecting Chinese Economy During COVID-19 Based on Regression Model in SPSS

The epidemic has had a profound impact on Chinese economy. In order to explore which factors have played an important role in Chinese economic development, this paper uses Internet software such as SPSS to construct a regression model to process and analyze economic data and evaluate its impact. First, we use SPSS software to construct a regression equation model that affects China's economic development. After that, we select four new factors affecting China's economic development : personal consumption level, financial data governance level, big data support capabilities, and robotics capabilities. After that, we use the SLOPE regression function model to calculate the P value, the correlation coefficient beta and the variance var value, and derived the regression parameter equation from this. Finally, based on regression equations and regression coefficients, new suggestions for China's economic development are put forward. © 2021 IEEE.

Communicative strategies for building public confidence in data governance: Analyzing Singapore's COVID-19 contact-tracing initiatives

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary analyzes Singapore's contact-tracing initiative to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program encompassing its proprietary TraceTogether app and physical token initially triggered vociferous public criticisms of Big Brother style surveillance. Using a dialogic communication framework, we analyze the TraceTogether initiative to interrogate the communicative strategies that were used to overcome public resistance. We argue that these strategies reflect a top-down approach that prioritizes transactional dissemination of information, in line with Singapore's technocratic stance toward governance. We further assert that such communicative tactics represent missed opportunities to foster public confidence in social data governance through greater trust building. We propose solutions for more dialogic communicative forms that build trust, so that officials can develop a sound understanding of the public concerns, increase the level of public engagement, and incorporate public feedback into policies that govern data use.

How Can South Korea Teach, Lead, and Better Engage with the Asia-Pacific in Shaping Data Governance for the 5G Era?

This essay explores how South Korea might contribute to the crafting of a new strategy designed to strengthen and better align regional approaches to data governance. MAIN ARGUMENT South Korea has not always fit cleanly into various U.S.-, Japanese-, or Chinese-led efforts to shape data governance in the Asia-Pacific. Yet there are few countries as important to the success of any collaborative effort to improve and better align regional data governance practices. South Korea not only is at the forefront of designing and producing a range of next-generation technologies but also is in the midst of a multiyear effort to reform its own data governance policies to better deploy these technologies. Moreover, South Korea has been a champion of prior efforts within APEC and other forums to address barriers to information flows. Identifying opportunities to learn from, build on, and further sharpen Seoul's existing efforts would benefit both South Korea and numerous regional stakeholders. POLICY IMPLICATIONS States and international bodies interested in regional data governance can learn from South Korea's ongoing process of wrestling with the debates inherent in data governance and reforming its frameworks. These insights include how public policy can benefit from inviting input from industry, civil society, and other stakeholders outside government into decision-making processes. Both formal legislation and statements by the Moon administration are suggestive of South Korean goals for data governance that overlap with U.S. priorities, such as aims to safeguard privacy rights, bolster digital development, and enable secure e-commerce. Still, divergences remain between how both countries have dealt with potential trade-offs in these goals, which may complicate efforts to advance joint regional leadership. In light of the novel and complex questions that have arisen during the Covid-19 pandemic, South Korea should increase its engagement with an array of other countries reviewing their uses of big data tools in managing the crisis. Dialogue on this issue could take place on a bilateral basis-including with Taiwan, Singapore, Australia, and India-or within multilateral groupings, such as APEC or the G-20.

COVID-19, policy change, and post-pandemic data governance: a case analysis of contact tracing applications in East Asia

In an era of digitalization, governments often turn to digital solutions for pressing policy issues, and the use of digital contact tracing and quarantine enforcement for COVID-19 is no exception. The long-term impacts of the digital solutions, however, cannot be taken for granted. The development and use of data tools for pandemic control, for example, may have potentially detrimental and irreversible impacts on data governance and, more broadly, society, in the long run. In this paper, we aim to explore the extent to which COVID-19 and digital contact tracing have led to policy change in data governance, if at all, and what the implications of such change would be for a post-COVID world. We compare the use of contact tracing and monitoring applications across mainland China, Hong Kong, and Singapore to illustrate both the enormous benefits and potential risks arising from the design of contact tracing applications and the involvement of stakeholders in the various stages of the policy cycle to combat the COVID-19 pandemic. We argue that, while COVID-19 has not changed the nature of issues, such as public trust in data governance, the increasing involvement of big tech in data policies, and data privacy risks, it has exacerbated those issues through the accelerated adoption of data technologies.

The Social Data Foundation model: Facilitating health and social care transformation through datatrust services

Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.

Open Data Governance in the Time of the Pandemic

This paper investigates the state of open government data in the Philippines by comparing access to health information during the COVID-19 pandemic with available open data prior to it. It first assesses the availability and demand for government data through Freedom of Information (FOI) requests and data posted in government Open Data platforms. It then compares this with the emerging lessons from the creation of the health data hub during the pandemic. It analyzes it by considering data openness across three dimensions: content, people, and process. The openness of content subscribes to the accepted definition of data being free to access, and free to manipulate. Openness to people refers to who can actively participate and/or collaborate. Openness of the process pertains to whether the processes involved is transparent and whether the process is open to inputs from participants. It considers lessons from the pandemic as a way forward for more systematic data sharing for the whole of government in the future. © 2021 ACM.

Internet of Healthcare: Opportunities and Legal Challenges in Internet of Things-Enabled Telehealth Ecosystems

The COVID-19 public health crisis has accelerated the transformation of health systems to become more closely tied to citizens/patients and increasingly dependent on the provision and use of telehealth services. Internet of Things (IoT)-enabled telehealth systems (deployed in conjunction with AI systems) could facilitate the smart transformation of healthcare from a merely reactive system to a data-driven and person-centred system that provides remote health diagnosis, monitoring and treatment services, integrated real-time response solutions, as well as prospective insights. However, the realisation of these health-related benefits requires the processing of vast amounts of data concerning health. These operations and the use of new enabling technologies raises significant legal concerns and questions the applicability of existing/proposed legal concepts. For this reason, the research analyses the adequateness of EU privacy, data protection, data governance, AI governance and other regulatory rules in IoT-enabled (and AI-augmented) telehealth systems. In addition, the research aims to identify technical and organisational measures (best practices), which could facilitate the implementation of normative principles in these information systems in an effective manner. © 2021 ACM.

Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis.

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies-homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union's General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.

The Ethical Digital Surgeon.

This viewpoint explores the ethical and regulatory consequences of the digital transformation of the operating room. Surgical robotics is undergoing significant change and future advances will center around the capture and use of data. The consequences of creating this surgical data pipeline must be understood and digital surgical systems must prioritize the safeguarding of patient data. Moreover, data protection laws and frameworks must adapt to the changing nature of surgical data. Finally, digital surgeons must understand changing data legislation and best practice on data governance to act as guardians not only for their own but also for their patients' data.

Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions.

Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed-i. e., racialized categories-to document racialized health inequities. This essay, written in the aftermath of the January 6, 2021 vigilante anti-democratic white supremacist assault on the US Capitol, calls attention to the two-edged sword of data at play, reflecting long histories of support for and opposition to white supremacy and scientific racism. As illustrated by both past and present examples, including COVID-19, at issue are both the non-use (Edge #1) and problematic use (Edge #2) of data on racialized groups. Recognizing that structural problems require structural solutions, in this essay I propose a new two-part institutional mandate regarding the reporting and analysis of publicly-funded work involving racialized groups and health data and documentation as to why the proposed mandates are feasible. Proposal/part 1 is to implement enforceable requirements that all US health data sets and research projects supported by government funds must explicitly explain and justify their conceptualization of racialized groups and the metrics used to categorize them. Proposal/part 2 is that any individual-level health data by membership in racialized groups must also be analyzed in relation to relevant data about racialized societal inequities. A new opportunity arises as US government agencies re-engage with their work, out of the shadow of white grievance politics cast by the Trump Administration, to move forward with this structural proposal to aid the work for health equity.

Indigenous Peoples' Data During COVID-19: From External to Internal.

Global disease trackers quantifying the size, spread, and distribution of COVID-19 illustrate the power of data during the pandemic. Data are required for decision-making, planning, mitigation, surveillance, and monitoring the equity of responses. There are dual concerns about the availability and suppression of COVID-19 data; due to historic and ongoing racism and exclusion, publicly available data can be both beneficial and harmful. Systemic policies related to genocide and racism, and historic and ongoing marginalization, have led to limitations in quality, quantity, access, and use of Indigenous Peoples' COVID-19 data. Governments, non-profits, researchers, and other institutions must collaborate with Indigenous Peoples on their own terms to improve access to and use of data for effective public health responses to COVID-19.