Big Data Regulatory Legislation: Security, Privacy and Smart City Governance

The articles in this special section focus on Big Data as it impacts future smart cities. The world is experiencing a period of extreme urbanization. Moreover, this process will continue, and the global urban population is expected to double by 2050. Smart city has been proposed to improve the efficiency of services and meet residents’ needs for better quality of life. Essentially, smart city integrates the Internet of Things and emerging communication technologies such as fifth generation (5G) solutions to manage the citys’ assets, including transportation systems, hospitals, water supply networks, waste management, and so on. Therefore, smart city is driving innovation and new technologies, especially big data technologies for the big data era. In the future smart city, there is an urgent need to address the following issues: how to design algorithms to process mass data and how to utilize big data to improve the quality of service (QoS) for future smart cities.

In this developing paradigm, heterogeneous network infrastructure is combined with ubiquitous devices for sensing, smart control systems and big data analytics to create “smart cities.” Enhancing the overall quality of life is one of their main goals and they do so through smart services they offer in a broad range of areas such as transportation, healthcare, entertainment (including virtual reality), the environment (including pollution) and energy. To be able to offer those services, the job of big data and their analysis is complicated, since it allows for the extraction of useful insights from the enormous amounts of data that the smart cities produce. The current state-of-the-art research efforts aimed at big data analytics in a smart city setting are investigated and presented in this chapter. In particular, a comprehensive literature review on the big data analytic frameworks for smart cities is provided. Following that, a short talk on IoT and big data is briefed. Owing to different data that the smart cities collect, which frequently have contradictory processing needs, appropriate investigative methods based on the data type are also recommended, depending on the data type. The many smart city applications were investigated and a perspective on the difficulties associated with smart city development was developed. In this chapter, the methods addressing the smart city issues and development are examined, and the possibility for the employment of integrated IoT and big data analytics is presented. Finally, as a case study, a briefing on a call service application for the fire department in a smart city is provided to round up the presentation.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

In the quest for sustainable urban development, smart cities are increasingly harnessing cloud computing and big data analytics to optimize energy usage and drive policy initiatives. This review explores how leveraging these technologies can transform energy management within urban environments. Cloud computing provides the scalable infrastructure necessary for processing and storing vast amounts of data generated by smart sensors and IoT devices in real-time. By utilizing cloud-based platforms, cities can ensure the efficient aggregation, analysis, and dissemination of energy data, facilitating informed decision-making and policy formulation. Big data analytics plays a crucial role in this framework by enabling the analysis of complex and voluminous datasets to uncover patterns and insights that traditional methods may miss. Through advanced analytics, cities can gain a deeper understanding of energy consumption trends, predict peak demand periods, and identify opportunities for efficiency improvements. This data-driven approach allows policymakers to design and implement targeted energy policies, optimize resource allocation, and reduce operational costs. Key benefits of integrating cloud computing and big data analytics in energy optimization include enhanced real-time monitoring capabilities, improved accuracy in demand forecasting, and more effective management of energy resources. The ability to analyze and visualize large datasets supports the development of dynamic policies that adapt to changing conditions and promote energy efficiency across various sectors. Despite these advantages, challenges such as data privacy concerns, the need for robust cybersecurity measures, and the requirement for skilled personnel to interpret complex data must be addressed. Additionally, ensuring equitable access to these technologies across diverse urban areas is essential for maximizing their impact. In conclusion, the strategic application of cloud computing and big data analytics offers significant potential for optimizing energy use in smart cities. By embracing these technologies, urban centers can drive more effective and responsive energy policies, contributing to broader sustainability goals and enhancing overall quality of life.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

Several countries are planning to adopt the idea of smart city in their towns and enforcing large-scale data projects that promotes smart city features to achieve the recommended level of sustainable development to enhance the quality of life. The smart city tends to mean an advanced technological area that would be capable of understanding the environment by examining the data so it can improve the livability. The smart cities allow different kinds of wireless sensors to gather massive amounts, full speed and a broad range of city data allow the use of big data technology to learn important observations to control resources, services, and infrastructure effectively. The smart cities use numerous innovations to increase the efficiency of fitness, travel, resources, education and public infrastructure, resulting in a higher comfort level for their residents. Big data analytics is one of the most recent technologies with great potential to improve smart city facilities. Big data is a huge amount of data collected from different sensors, smart objects or smart devices connected with the Internet of Things (IoT). Smart cities allow regional officials to communicate directly to both society and city services as well as to track what might be happening in the city or how the city is progressing. This chapter presents a comprehensive discussion on the fundamental of data analytics, challenges, and a case study of NEOM smart city in Saudi Arabia.

PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

Building a smart city that follows sustainability goals enhances the quality of life and preserves environmental, human, and social capital. Yet, existing smart sustainable city projects have concentrated on the technological dimensions of smart cities such as using big data or smart devices to follow sustainability goals. Currently, there is no comprehensive category of smart sustainable city indicators in the literature. This paper aims to discover these indicators by considering the common features of sustainability and smart city concepts. Two rounds of the content analysis technique were employed to investigate semantic, lexical, and conceptual relationships between smart city and sustainability indicators. This paper employed the Sustainable Development Indicators suggested by OECD and the Smart City Index Master by Cohen as the two main groups of indicators. The findings offer a novel set of indicators that enables policymakers and researchers to consider the smartness and sustainability of their projects simultaneously. This includes socio-cultural, economic, environmental, and governance categories with 28 associated indicators. The outcome of this paper offers a unique combined category of smart sustainable city indicators by considering the key elements of sustainability and smart city concepts. Academics and policymakers can also employ this set of indicators as a guideline to build a smart sustainable community.

‘Smart cities’ is a term that has gained traction in academia, business and government to describe cities that, on the one hand, are increasingly composed of and monitored by pervasive and ubiquitous computing and, on the other, whose economy and governance is being driven by innovation, creativity and entrepreneurship, enacted by smart people. This paper focuses on the former and, drawing on a number of examples, details how cities are being instrumented with digital devices and infrastructure that produce ‘big data’. Such data, smart city advocates argue enables real-time analysis of city life, new modes of urban governance, and provides the raw material for envisioning and enacting more efficient, sustainable, competitive, productive, open and transparent cities. The final section of the paper provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns: the politics of big urban data, technocratic governance and city development, corporatisation of city governance and technological lock-ins, buggy, brittle and hackable cities, and the panoptic city.

PurposeBig Data has become crucial for fulfilling the vision of smart cities; however, there are several challenges associated with its use. This study aims to provide a holistic view of challenges of using Big Data in smart cities and the solutions to deal with them by systematically selecting, reviewing and synthesizing the literature in this area.Design/methodology/approachA systematic literature review was conducted by following a rigorous search protocol that identified a total of 310 peer-reviewed academic research papers. These papers were subsequently filtered and finally 38 relevant papers were selected.FindingsA number of major challenges (human dynamics, privacy, security, volume, velocity, variety, veracity and value) associated with Big Data in a smart city and the respective solutions to address them were identified. This study primarily contributes in providing the process of effectively managing Big Data in smart cities.Practical implicationsThe study provides valuable information to the Big Data practitioners by illustrating the process of effective management of Big Data in smart cities. This study further provides the directions to the future researchers.Originality/valueThe study is original and is based on the existing literature and its interpretation.

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

The big data revolution is set to erupt in both smart cities and sustainable cities throughout the world. This is manifested in bits meeting bricks on a vast scale as instrumentation, datafication, and computation are routinely pervading urban environments. As a result, smart sustainable urbanism is becoming more and more data-driven. Explicitly, big data computing and the underpinning technologies are drastically changing the way both smart cities and sustainable cities are understood, operated, managed, planned, designed, developed, and governed in relation to sustainability in the face of urbanization. This implies that urban systems are becoming much more tightly integrated and urban domains much more highly coordinated while more holistic views and synoptic city intelligence can now be provided thanks to the possibility of drawing together and interlinking urban big data as well as reducing urban life to a form of logic and calculative procedures on the basis of powerful computational algorithms. These data-driven transformations are in turn being directed for improving, advancing, and maintaining the contribution of smart sustainable/sustainable smart cities to the goals of sustainable development. This chapter provides a comprehensive, state-of-the-art review of smart sustainable/sustainable smart cities as a leading paradigm of urbanism in terms of the underlying foundational components and assumptions, research status, issues and debates, research opportunities and challenges, future practices and horizons, and technological trends and developments. As to the findings, this chapter shows that smart sustainable urbanism involves numerous issues that are unsolved, largely ignored, or underexplored from an applied theoretical perspective. And, a large part of research in this area focuses on exploiting the potentials of big data technologies and their novel applications as an effective way to mitigate or overcome the issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches. The comprehensive overview of and critique on existing work on smart sustainable urbanism provides a valuable reference for researchers and practitioners in related research communities and the necessary material to inform these communities of the latest developments in the area of smart sustainable urban planning and development. The outcome of this topical review will help strategic city stakeholders to understand what they can do more to advance sustainability based on big data technology and its novel applications, and also give policymakers an opportunity to identify areas for further improvement while leveraging areas of strength with regard to the future form of sustainable smart urbanism in the era of big data.

The concept of smart cities transformed as global implementation is hampered by a host of technical, economic, and government road barriers. In addition, the integration of Internet of Things (IoT) and big data technology has opened new horizons in terms of smart home technology and smart city development that is yet to be explored. IoT is a technology that allows devices to connect to the Internet, and applications are designed to meet the needs of the community. Communities are very interested in using this and other information technology in cities to build smart cities in recent years. It is important to consider big data analytics (BDA) when using IoT in a small ecosystem because there is less data to be managed than a big city where there is data from more smart devices and people BDA aims to make data informed decisions on the basis of raw facts provided. Aside from the fact that there are many articles in the literature that talk about the BDA as a decision-maker in smart cities, general research on BDA and smart cities focuses on the technical or intellectual properties of a particular smart city.

As the world population grows along with increase in urbanization, cities are getting more and more populated and increasing demands on various natural and man-made resources. Making cities smart through the appropriate application of the plethora of innovative new technologies and paradigms would help mollify potential environmental problems/constraints in such cities. Besides their significance in solving environmental problems, smart cities also aim to improve citizen's quality of life and efficiency of public services by optimizing the costs and resources involved. Smart city applications are founded on various enabling technologies and processes related to communication and networking, real-time control and big data analytics, to name a few. These constituents of a smart city infrastructure need to be integrated appropriately and seamlessly for provisioning efficient services to citizens. Each enabling technology has its own unique properties. The interplay and interaction among smart city constituents and their unique properties raise unique security and privacy challenges. In this chapter, we overview enabling and emerging technologies and security and privacy challenges for smart cities. Ensuring authorized accesses to these constituents is critical for ensuring the security of smart cities. We present existing solutions to these challenges mainly from the perspective of access control (AC) with a special focus on risk management, trust, insider threats and secure interoperation. Finally, we present future research directions.

Big data and artificial intelligence based early risk warning system of fire hazard for smart cities