Sharing big data using blockchain technologies in local governments: Some technical,organizational and policy considerations

Abstract

Governments have vast data resources related to a wide-variety of policies and programs. Integrating and sharing data across agencies and departments can add value to these data resources and bring about significant changes in public services as well as better government decisions. However, in addition to the lack of standards and an adequate information architecture, the main obstacles to a centralized government data-sharing strategy are security and privacy concerns. Blockchain - a decentralized peer-to-peer distributed ledger technology - provides a new way to develop sharing mechanisms. In addition, blockchain-based systems are difficult to tamper with and are highly traceable. Based on the current problems of a big data center in the city of Ningbo, China, this paper identifies limitations of this approach and explores the potential of some data sharing mechanism based on blockchain technology. Our analysis describes some potential advantages and the feasibility of using distributed data sharing and automated management mechanisms based on blockchain smart contracts. We also explore implementation challenges and provide some practical recommendations.

Keywords

Government big data blockchain technology sharing mechanism centralized distributed

1. Introduction

As the digital age progresses, big data has emerged as a national strategic resource (Janssen et al., 2017; Mergel et al., 2016; Chatfield & Reddick 2017; Moody et al., 2019; NITRD, 2018). While private companies – social media companies, most famously – collect a wide variety of data about people, government departments also accumulate copious amounts of data related to citizen livelihoods, economies, medical care, transportation, and social security among many other aspects of life. More specifically, government agencies are collecting data from cell phones, sensors, and other devices in order to better understand patterns of traffic, levels of pollution, and the probabilities of flooding, among others (Chen & Zhang, 2014; Janssen et al., 2017; Krishnamurthy & Desouza, 2014). We refer to all these data here as, “government big data,” or “GBD” (LaBrie et al., 2018; Kim et al., 2014).

Opening, sharing, and utilizing GBD is an important subject in current research (Charalabidis et al., 2019; Bertot et al., 2014; Bedini et al., 2014; Hong et al., 2019; Kim et al., 2014). For government departments with large amounts of data the challenges are akin to mastering large numbers of physical resources or assets. However, the value of GBD is only achieved when it is developed and utilized by data management and analytical tools in appropriate ways (Puron-Cid et al., 2016; Chen et al., 2012; Watson, 2014). Otherwise, government big data could become a “sleeping” asset, which means, the value of the data could never be mined or applied appropriately. So, it is important for departments to revitalize and manage this asset. State and local governments are faced with requirements to make data available for sharing. The potential solution to this challenge begins by recognizing that data is a ‘shared asset’, this is why data governance is essential – as a set of practices, policies, standards, and guideposts that provide a foundation for deriving value and insight from data (Randy, 2016).

GBD governance has the goal of being able to exploit the potential value of collected data (Andrews, 2019; Klievink et al., 2016; Morabito, 2015; Ku & Gil-Garcia, 2018; Bernardmarr, 2015). GBD governance requires multiple parties – producers, collectors, managers, and users, among many others – to participate (Joseph & Johnson, 2013; Clarke & Margetts, 2014; Harrison et al., 2018). To use the example of the hospital, an institution may need to share health data with the courts, with insurance companies, or with the employers of a patient (Aashish, 2019). The development, utilization, and governance of GBD is an application scenario with multiple subjects, multiple parties, different permissions, and numerous links among different departments and agencies with differing priorities and levels of expertise. Under ideal conditions, GBD governance can ensure that the right data is available, at the right time, and used in the right context (Klievink et al., 2016; Cronemberger & Gil-Garcia, 2019; Custers & Bachlechner, 2017).

In reality, government departments often face problems when attempting to share GBD (Welch et al., 2016; “Big Data for Development: Opportunit $\ldots$ ; Sukumar & Ferrell, 2013). First are security concerns. Sharing or opening a department’s data could imply a disclosure of government secrets and generate concerns among staff (Moody et al., 2019; Welch et al., 2016; Kim et al., 2014). Second, data may be the lifeblood of a department. If data is disseminated among many users, the department’s core identity – and need for its existence – may be lost (Wood, 2014). Finally, data may require the stewardship of citizen privacy (Janssen and van den Hoven 2015; “Big Data for Development: Opportunit $\ldots$ ; Kim et al., 2014). We do not argue that this is an exhaustive list, but we think that these aspects have limited the development, utilization, and governance of GBD (“Big Data for Development: Opportunit $\ldots$ ). However, we also argue that the technical characteristics of blockchain technology can alleviate some of these concerns. This is because blockchain can improve the security and effectiveness of sharing GBD (Abdullah, 2017; Cheng, 2017; Karafiloski, 2017; Ølnes, 2017). Thus, the purpose of this paper is to show the potential of blockchain technologies to address some of the main limitations of centralized information sharing mechanisms in local governments.

This paper is organized in six sections, including the foregoing introduction. Section two presents a brief review of existing literature, with a particular emphasis on government information sharing, big data, and the potential of blockchain. In Section 3, we briefly describe our research design and methods. This paper is based on the case of the city of Ningbo, China. Section 4 presents the analysis and main results of our study. In Section 5, we discuss how blockchain technology could be used in government big data governance and provide some specific recommendations for practice. Finally, Section 6 presents our conclusion and suggests areas for future research about this topic.

2. Big Data governance, information sharing, and using blockchain technology in the public sector

Based on a review of existing literature, this section briefly presents the background, concepts, and challenges of government information sharing, big data governance, and blockchain technologies. We explore some technical and organizational challenges facing the GBD’s governance from the perspective of information technology development. We argue that this could be, at least partially, solved through technical solutions and governance models.

2.1 Government information sharing

Information sharing refers to exchanging or giving of other government agencies access to program information, which would normally be used only internally (Andersen & Dawes, 1991; Dawes, 1996). With the development of digitalization in government, there have been deeper understandings of government information sharing (Gil-Garcia et al., 2009; Gil-Garcia & Sayogo, 2016). Gil-Garcia et al. (2010) propose that government information sharing is a multidimensional concept with four core components: trusted social network, shared knowledge and information, integrated data, and interoperable technical infrastructure. This concept shows the combination of technical and non-technical components in government information sharing and provides a motivation to study the interaction among the components (Gil-Garcia et al., 2009, 2019; Cronemberger & Gil-Garcia, 2019; Scholl & Klischewski, 2007).

More recently, other researchers focus on identifying the factors affecting the success of government information sharing. For instance, Yang and Maxwell (2011) provide a summative framework to identify factors affecting information sharing at interpersonal, intra-organizational and inter-organizational levels. Bigdeil et al. (2013) provide an analysis of the factors affecting local government participation in a government information sharing project. They also provide recommendations for government officials and public managers to decide whether to participate in an information sharing project or not. However, the implementation of government information sharing not only faces challenges from the decision-making process (Pardo et al., 2008; Cronemberger et al., 2017), but with specific administrative micro-operation processes also (Tuya M D & Tuya M L, 2019). During the government information sharing adoption process, bureaucratic governance structures of project approval(s) can reduce the efficiency of project building (Luna-Reyes et al., 2007; Gil-Garcia & Sayogo 2016).

The use of policy tools could help establish common goals and the operational process of government information sharing, but there may be a lack of problem-solving ability to correct power imbalances and to reduce the risk of information security problems (Anderson et al., 2017; Bellamy et al., 2007; Cuganesan et al., 2017). Local governments have tried to build centralized information system to directly connect information providers and receivers to solve complex problems (Yang et al., 2012, 2014). However, those governments still need to improve their collaborative ability to balance the conflicts between power structures and sharing needs (Scholl et al., 2012; Wang, 2018).

2.2 Big data and government information sharing

“Big Data,” refers to a holistic approach to manage, process and analyze data with a high level of volume, variety, velocity, veracity and value (Chen et al., 2012; Janssen et al., 2017). Compared with developed countries such as the United States and the United Kingdom, which adopted country-wide digital strategies (The White House: http://www. whitehou…), big data in governments from developing countries lack financial and human resources (Hilbert, 2015, 2016; Morabito, 2015; Sivarajah et al., 2017; De Mauro et al., 2017), while also facing technological and organizational challenges (Chen & Hsieh, 2014; Klievink et al., 2016; Aggarwal, 2016).

Big and open government data can foster collaboration, create real-time solutions to challenges in agriculture, health, transportation, and more, but current information policy frameworks face many challenges (Wu et al., 2018; Andrews, 2019; Gamage, 2016; Bertot et al., 2014). Although governments can analyze various types of data – resulting in new evidence to support decision-making; (Janssen et al., 2017; Kim et al., 2014) – high technical thresholds turn data analysis processes into a “black box,” and governments need to promote transparency and accountability to safely utilize sensitive and private information (Wang et al., 2019). Such a problem is made evident by the lack of empirical studies exploring the micro-operation mechanisms of the advanced technologies used in the public sector (Mergel et al., 2016; Chen & Zhang, 2014; Aggarwal, 2016).

The collection and integration of big data also needs the support of cross-boundary collaboration in governments (Klievink et al., 2016; Aggarwal, 2016; Bertot et al., 2014). This collaborative process not only needs to meet data and information requirements (Höchtl et al., 2016), but also needs to promote credibility among departments (Bertot & Choi, 2013). In the real world, traditional management models and bureaucratic management regulations have delayed the building of sharing technology (Mergel et al., 2016; Dawes et al., 2009). The time required for implementation reduces the efficiency of technology development (Luna-Reyes et al., 2007).

While the use of policy tools can help establish common goals and implementation processes for information sharing, it does not guarantee participant consensus on what is appropriate information to be shared or overall consistency, thus hurting collaborative participation (Bellamy et al., 2007). Existing centralized information systems in local government attempt to find ways to connect information providers and information users directly (Aggarwal, 2016; Harwood & Mayer, 2016; Yang et al., 2014), but there is still a gap between existing technology and the need of sharing big data within and across government agencies.

2.3 What is blockchain technology?

Blockchain is a decentralized, distributed ledger technology. The ledger is formed by data block links that are maintained by all participants. Each chain participant has a copy of the entire chain and works off of agreed upon rules to record and trace the entire process. Individual participants cannot modify the data within the chain. Data on a blockchain is traceable and irreversible. When data is added to the chain or around a certain asset, each transaction or operation generates a new block. Once a data block is generated, it is approved by all participants, asymmetric encryption ensures that data cannot be tampered with, and the block is timestamped.

The hash value of each packet on a chain is unique and can verify the authenticity of the packet. At the same time, asymmetric encryption technology is used between parties, which can divide roles, refine operation rights, and ensure data privacy (Kypriotaki et al., 2015). In addition, blocks are generated with the consensus of all blockchain participants. On a blockchain, a data exchange record is recognized, transparent and traceable by all participants while the source of data and circulation paths can be recorded and traced. Every update and modification of data can be tracked. A hash algorithm can be used to verify the integrity of data, further ensuring and improving circulation quality. All of these aspects help with data validation (original source, management rights, access rights, usage rights, etc.). This combination can potentially eliminate concerns about security and privacy and remedy issues that many government departments raise when it comes to information sharing (Olnes et al., 2017).

Blockchain technology is a potential technology that can easily improve government services and foster fair and transparent citizen rights. Blockchain for government can optimize the governmental processes and offer secure, yet efficient sharing (Anwar, 2019). The use of blockchain technology also promotes data circulation, record generation, exchange, transfer, updates, development and utilization (Beaman et al., 2019; Yang et al., 2018). This addresses data-sharing concerns of government departments. Sharing and opening up all departments can eliminate concerns for each individual department (Dawes et al., 2009; Welch et al., 2016). Finally, the application of blockchain-based smart contract technology can automatically manage and implement agreed upon data sharing that is open to government departments, reduce human intervention in the operation process, and create an open trustworthy data sharing environment. Overall, blockchain technology allows for decentralized, multi-party participation, and common maintenance, which combine together to enhance trust and offer a solution to clearing the impediments of the development, utilization, and governance of information sharing (Marsal-Llacuna, 2018; Li et al., 2018).

2.4 Applying blockchain technology to big data sharing in the public sector?

Nowadays, governments around the world are very interested in developing blockchain services and applications. For instance, the Dutch government set up 40 pilot projects at multiple levels of government (e.g., ministries, municipalities). Their pilot projects built blockchain based-systems to store and process public information such as digital identity, judicial decision, and public financing. Similarly, Estonia developed a blockchain platform to protect electronic data from hacking, manipulation and system failures. Researchers expect that blockchain-based information systems will be useful for government organizations to exchange information directly through peer-to-peer platforms (Ølnes et al., 2017).

The development of GBD has intensified technical and organizational challenges faced by information sharing, and blockchain technology provides a potential solution (Andolfatto, 2018). Specifically, Ølnes et al. (2017) summarize the benefits of blockchain technology and they think that distributed data processing could change the underlying technical logic of information sharing to avoid the push-pull dynamic power imbalance between administrative pressure and the information sharing requirements (Wang et al., 2018). Blockchain technology could simplify the management of trusted information, making it easier for government agencies to access and use critical public-sector data while maintaining the security of this information.

An important function of government is to maintain trusted information about individuals, organizations, assets, and activities (Léveillé & Timms, 2015; Janssen & van den Hoven, 2015; Joo et al., 2018). As a decentralized and distributed technology, blockchain technology has a potential both to explore the value of big data and to auto-distribute the data right among multiple participators (Abdullah et al., 2017; Karafiloski & Mishev, 2017; Liu & Zou, 2019). The utilization of blockchain technology in information integration and sharing has the potential to identify the originals of the various data and track the process of data right distribution among information providers and users. Since the data was extracted from the citizens and cities’ sensors, the agencies only have data management rights instead of ownership. In order to forbid the disclosure of sensitive information and forbid the abuse of data management right, the government uses blockchain technology to trace the life cycle of big data integration and sharing. There are a number of blockchain tools and technologies that government agencies can implement today to protect critical data and improve the management of records associated with property ownership and incorporation (Using blockchain to improve data mana $\ldots$ , 2017).

One IDC study has focused on the relationship between blockchain and data management. Blockchain technology has already been tested for data sharing in clinical research (Benchoufi & Ravaud, 2017) and intelligent vehicles (Singh & Kim, 2017), and blockchain has shown potential for building trust, preserving privacy, and enhancing security (Park & Park, 2017). However, such research is still mostly conceptual, lacking empirical studies. In addition, critical questions remain, such as: (1) What benefits and challenges does blockchain technology provide for government information sharing in contrast to a centralized information sharing strategy? and (2) How do data management solutions in the administrative process help to face technical and organizational barriers? Developing knowledge about these and other related questions requires real-life cases that provide in-depth understanding and insights.

3. Research design and methods

This section briefly describes the research design and methods used in this study. This research is based on semi-structured interviews and the analysis of official documents. These methods were used to understand the case study of the city of Ningbo, in southeast China. Following we explain the data collection methods and also provide a brief description of the case in terms of their current situation and needs, particularly in relation to the challenges they are facing when dealing with a centralized information sharing strategy.

3.1 Data collection methods

We use semi-structured interviews with government officials and city government documents as our main data resources for this study. Seven people were interviewed for our study and they were selected based on their involvement or relationship with the centralized data management mechanisms. Three of them were public managers in charge of department’s data, two of them were operators of the cloud big data center, and two of them were technical directors of the cloud big data center. The interview included questions related to their current needs in terms of big data analysis and security, the process of data integration, data utilization and data sharing, some of the advantages of a centralized strategy, and some of the challenges they were currently facing. The interviews were recorded and transcribed by the research team and the data were coded using predefined categories based on the main concepts and relationships used in this study. In terms of city documents as the other source of research data for this study, we obtained different types of documents and other information provided by the research institutions, including government internal agreements, government work logs, and the information available in the public website. The documents were also coded and we used the information from the documents to complement the data from the interviews.

More specifically, the questions in the interviews attempted to understand GBD governance as a complex socio-technical system involving management, policy, technology and other important aspects. From a data perspective, GBD involves data collection, storage, aggregation and integration, security, quality and standards, among others. From the governance perspective, GBD involves opening, sharing, and market utilization. The long term goal of combining security, privacy, openness and social use of GBD governance is a challenge, but in the short-term data fusion and sharing between government departments is a more attainable goal which can then be used to further those larger and long-term ideals. The interviews asked participants questions related to all these important aspects of information sharing, big data, and the potential of blockchain in the context of local governments.

3.2 Brief description of the case

As mentioned before, for this paper we analyzed the case of the city of Ningbo, which is a coastal city in southeast China. The city of Ningbo currently has a smart city development plan named “Building the Brain of the City”, which needs to collect and integrate different types of data from all aspects of the city and then use it. The data is currently stored and managed by a cloud-based big data center, which is run by a specialized agency called the “Big Data Bureau”.

Case studies are normally chosen either because they are critical, unique, revelatory, or exemplifying in that they will provide a suitable context for the research questions to be answered and allow the researcher to examine key social process (Yin, 2003). According to the purpose of this paper, we select the city of Ningbo as the case for this study. This city is chosen for two reasons. One of the reasons is that Ningbo is one of the first Chinese cities that is trying to introduce blockchain technology into the big data sharing process, which means that the analysis results of this case could provide some new insights on this topic. The other reason is that we have a long participant observation history with the city of Ningbo since two of the authors joined their big data sharing project implementation in 2016 and they are still collaborating with the city. Given this long-lasting relationship, it has been easier to have access and do the interviews and understand the insights from the case.

The current structure of the case in terms of data sharing has important limitations and blockchain technology has the potential to play a role in the reform of their GBD governance. In China, many local governments are pooling data from multiple government departments for centralized management. It has proven to be effective to some extent. However, this centralized approach increases the risk of data breaches, data security, and creates friction among government departments which feel a sense of data ownership and has stymied some data collection and enthusiasm. Through the analysis of the case (including the interviews and the documents) and considering the characteristics of the blockchain technology, we obtain evidence on its potential, as well as some challenges for implementation in the city government context.

4. Analysis and results

This section presents the main results of our analysis. Based on the case of the city of Ningbo, there are several potential problems with centralized big data sharing mechanisms. Following we briefly describe the characteristics of the centralized information sharing structure as well as some of the problems they are currently facing. Ningbo is a sub-provincial city in Zhejiang province in China, and it has characteristics that are similar to other local governments, including its data governance.

4.1 A centralized sharing mechanism: The case of Ningbo

Following the principle that “one type of data comes from an authoritative department and the authority department is responsible for updating and maintaining”, the city of Ningbo collectively aggregates shared data from different departments, storing them in a government cloud data center according to Chinese information security management requirements.

An application catalogue gradually integrates data from various government agencies and/or departments such as population, legal person status, geography, credit, health, education, and real estate ownership. Shared data convergence centers gradually formed a shared application mechanism with the municipal cloud data center, which serves as a data hub and is supplemented by inter-departmental data exchanges, as shown in Fig. 1. This improved the comprehensive data sharing efficiency of the city. The municipal cloud data center provides data services to various application systems, and the user group of the center includes the business system in each department. Users can be divided into three categories: data provider, data center manager, and data user.

Figure 1.

Centralized sharing mechanism of government big data.

(1)

Data provider – A government information system or unit that provides shared data sources to the relevant municipal cloud data center. The data provider is responsible for data resource registration, data provision, data quality tracking, and self-data usage review.

(2)

Data center manager – The data center manager is mainly the administrator of the cloud data center. The central administrators are divided into three categories: 1) system administrators, 2) data administrators, and 3) operation and maintenance personnel. The main responsibility of the system administrator is user management, role management, rights management, system parameter configuration, platform monitoring, and data management and control platform monitoring and management of the platform operation.

The main responsibility of the data administrator is to monitor the life cycle of the platform process data, such as the configuration of data collection rules on the collection platform, the quality and integrity of the collected data, the design and setup of data processing rules on the integration platform, and the overall quality output after processing integration. Monitoring and evaluation, analysis of data in the data platform and quality monitoring and evaluation of analytical reports, data sharing management, standard and specification development, order approval and abnormal data feedback are also responsibilities of the data administrators.

The main responsibility of operation and maintenance personnel is to configure data backup, support recoveries, hold data storage authority, maintain the platform, perform platform security monitoring, and handle system exception routines in the data storage management platform according to the rules established by system administrators and data administrators.

(3) Data users – Data users use a government information system or unit that shares data from a shared thematic library of the municipal cloud unified data architecture platform. The shared data is used to 1) display the required shared data and acquire the thematic library method in the shared data resource directory; 2) initiate a data sharing request, or 3) use the shared service that has been acquired through the visual management interface of the municipal cloud. At the same time, a user can also know the status and sharing status of the data resources or service resources that have been applied through the platform.

The shared data user selects the required shared data and its shared service in the shared data resource directory through the visual management interface of the municipal cloud. The bulk purchase can be added to the shopping cart; after the selection is completed, the data order is created and submitted to the municipal cloud review. The municipal cloud administrator reviews the order, and the user does not obtain the data until the order is approved. After approval, the flow is transferred to the municipal cloud leadership review; the municipal cloud leader reviews the order. The party continues to modify the order information, and the data order takes effect after the approval; the key requirements of the municipal cloud administrator generate the corresponding thematic database and the delivery manual; the shared data user shares the data from the municipal cloud according to the delivery manual.

The centralized government data sharing center is a large infrastructure development and system application project, covering a wide range of technical and management aspects. The centralized sharing method concentrates the data, but increases the risk of data security and disclosure. The center concentrates data management, but the responsibility for the data is not clear enough and the division is difficult. Following, we describe some of the challenges faced by the city of Ningbo and how blockchain technologies have the potential to help the city government address some of these current challenges and limitations.

4.2 Applying blockchain technology to big data governance: understanding the potential

For government units, data sharing across multiple departments is challenging, but possible through the centralized mechanisms. One of the interviewees highlighted the importance of having the consent of the data provider for information sharing: “We sign an agreement with the data provider, we will handle the confidentiality issue, and the data provider does not have to do it. The data provider is the owner of the original data. We provide the data to other departments with the consent of the data provider. The data will not be provided to the third party without the consent of the data provider. The premise of sharing the data is to ensure clarity in terms of the ownership of the data. Therefore, we provide data to and from other departments but always with the consent of the data provider.” Ningbo’s data sharing model is based on the set up of an independent organization that coordinates the overall situation and concentrates and aggregates data. This does not solve the data security problem since it does not have a solid nor defined division of data rights, responsibilities, and benefits. In addition, establishing a centralized data management organization increases operating costs. This is where blockchain technology could help the city. Given the complexity and intricate nature of government and public administration issues, those problems and limitations could be expressed in a variety of forms across several domains, including data security issues (Farahat et al., 2019; Alketbi et al., 2018), the scope of political and administrative accountability in handling and brokering data (Dencik et al., 2016; Aradau & Blanke, 2017; Ruppert et al., 2017), as well as their responsibilities related to citizens privacy (Crain, 2018; van Zoonen, 2016) and tangible and intangible costs of managing and using data (Al Nuaimi et al., 2015; Bates, 2018).

4.2.1 Data security problems

Governments are the main targets of hackers, and they try to pry into citizens’ privacy. They also disrupt or hurt organizations by stealing a large sum of money from them. When using distributed data storage on a protected network, it becomes more difficult for hackers to get in (Hasib, 2019). Blockchain technology also helps prevent possible data leaks. Once the data is stored on the blockchain, each point will need multiple permissions from other points in the network to access the data. It is therefore impossible for a cybercriminal to seize it (Aashish, 2019). In the case of Ningbo, data from all government departments is centrally located in a cloud data center. It will be catastrophic if there is a data leakage or hacking. With blockchain distributed technology, data does not need to be stored centrally, for blockchain is used to link all the requirements of each department and all operations related to data are performed on the blockchain. Each department acts as a node of the blockchain, using asymmetric encryption technology to interact. If there is a security breach, with blockchain technologies it would be easy to detect the problem and try to find a solution. Therefore, blockchain technologies can potentially address some of the security concerns that the city of Ningbo is currently facing with its centralized information sharing platform.

4.2.2 Politics and sense of identify in government agencies

In Ningbo’s centralized sharing mechanism, each department is required contribute its own data and then share it after the data is centrally stored. Each city department is worried about losing ownership of the data, especially the department where the data is the lifeblood of their daily operations, thus greatly reducing the incentives or the enthusiasm of the department for data sharing. With blockchain technology, data could still be stored in a distributed manner, without a centralized platforms and a single responsible agency. Thus, departments do not need to put their data on a centralized repository. The information on the blockchain accurately records the owner of the data with all members reaching a consensus, which it is very difficult to tamper with. Blockchain technologies could potentially help to solve the resistance to share information, by providing a way to do it in a relatively decentralized fashion and with clear definition of ownership rights.

4.2.3 Citizen privacy concerns

Ningbo’s centralized sharing mechanism and platform make it possible to integrate data from multiple departments. The data of many government departments are closely related to citizens and some of them could be considered personal such as social identifications and health-related information. Other data are not strictly personal, but combined with personal data could be used to identify individuals and their interactions with government such as traffic and public safety data. The fusion of data from multiple departments may be used to outline a “full profile” of each citizen, which clearly affects privacy. With blockchain technologies, the data on the chain is encrypted for storage and transmission, and the data off the chain can be stored by hash. The use of data is managed strictly and transparently, which can greatly reduce the risk of using data to create full profiles and address some of the citizen privacy concerns. Of course, blockchain technologies are not the complete solution for this, since there is a need for adequate policies and a clear governance structure, but they could be seen as one of the important elements.

4.2.4 Increased operational and total costs

In the case of Ningbo, the construction of cloud-based data centers and the centralized information sharing operations (among other technical and governance aspects) have represented a significant increase in overall costs. Cities similar to Ningbo could consider a more decentralized governance and technical architecture in order to avoid some of these additional costs. If blockchain technologies are used to build sharing mechanisms and platforms, all departments will be peer-to-peer, and the shared rules and regulations will be implemented using blockchain smart contracts, reducing manual intervention and thus reducing expenditures. Therefore, the key is not using blockchain only, but taking advantage of some its functionalities for local governments to be able to obtain benefits, but without increasing the costs to levels similar to the development of a centralized information sharing platform and its corresponding governance and manual processes.

5. Implications and practical recommendations

This study analyzes how blockchain technology can be applied to GBD governance. Data ownership, dissemination processes, transaction chains and other related information can be comprehensively recorded in distributed data blocks, where there is a consensus among all chain participants. There are practical applications of blockchain in GBD governance along with limitations and challenges. Next, we present a few practical recommendations.

5.1 Build a chain that is right for the goal

Blockchains can be divided into many categories, depending on the degree of openness: public chains, alliance/coalition chains, or private chains. A public chain is open to anyone and anyone can participate; an alliance/coalition chain is open to certain organizations, and a private chain is only open to an organization or individual. Different blockchains can be used depending on the degree of openness of GBD. For example, a national data center at a federal or provincial level might want to use a public chain, which is owned by the public and shared by the public. However, for local government departments, an alliance/coalition chain might be a better choice, while a private chain may be the best approach for data management within a single department. Therefore, it is best to build a chain for each type of data or select the right type of chain for the goal of the initiative. This could be related to the number and types of actors that need to be involved, but also to the types of data that would be shared among multiple entities.

5.2 Design a consensus mechanism based on the situation and actors involved

The basic requirements of a consensus mechanism are multi-party participation, responsibility, and ownership, so the entire life cycle of data can be managed and monitored. A good consensus mechanism also needs to be accompanied by incentives to encourage all parties to actively participate in managing data. Anyone can become an actor on a public chain, but on a private chain there needs to be an approval process from a central node. An alliance/coalition chain access can be approved collectively or by a majority proportion of established actors. Actors can leave chains freely. Once actors have joined a chain, when data is recorded the recorder can be awarded points. Points are then used to request data from the chain. Similarly, as a data provider, if shared data is requested and used, points can be rewarded to the provider. Therefore, it is very important to design consensus mechanisms that consider the situation and the specific actors involved. Incorporating the right incentives into the design is also important.

5.3 Rights management and trading rules should be based on smart contracts

Data rights management and transaction rules should be based on intelligent contracts and these can be implemented through chain coding, which is automatically executed during the transaction process, without human intervention. This ensures legal, reasonable and compliant data operations in real time. For example, in a multiple-department shared data model, each department obtains points by sharing its data (type, data volume, update frequency, etc.) through an agreed-upon specific integration mechanism. The chain can define scores and allow for higher permission requests and use of other department’s data. When a permission value is reached the data block of a request operation can be automatically generated, protocols take effect, and no manual intervention is required, which improves efficiency. However, since transactions will be automatically executed, it is very important to think carefully about the rules that need to be defined within the blockchain.

5.4 Select the right blockchain governance mechanisms according to your needs

While blockchain technologies may be a solution, with the myriad of government departments, varying degrees of informatization, diversity of data types and different data gathering standards, multiple blockchains may be needed to address each unique situation running contrary to a single-chain solution. Also, there are some GBD sets that are large and complex – video surveillance data, for example – and since blockchain technologies require all participants in the blockchain to keep a copy of the chain, there may not be enough sophisticated hardware to support participation for all actors. In this case, due to transmission efficiency concerns and storage space consumption, blockchain technologies may only be applied to the governance mechanism level, which plays an auxiliary role in the governance of GBD. This model could be referenced as a “light” governance mechanism based on blockchain. Storing all data on a blockchain could be a “strong” governance mechanism based on blockchain. Governments need to decide the best governance mechanism for their specific needs and limitations and, as mentioned before, this could also be related to the specific policy domain and to the type of data being shared.

6. Conclusion

Government big data (GBD) could be seen as a valuable asset for agencies and the society as a whole. However, the value generated by their use could be significantly higher if government agencies are willing and able to collaborate and share data across organizational boundaries. Sharing data across agencies and departments could be seen as a key component of GBD governance and, in general terms, this can be done following either a centralized or a decentralized strategy. Therefore, GBD governance could help increase the value of government data, make better decisions, and improve public services. However, there are some important shortcomings to a centralized sharing mechanism, as observed in the case of Ningbo, China. These challenges are related to data security problems, citizen privacy concerns, increased operational costs, and resistance of government agencies to collaborate and share information due to politics and fear to lose control of their own data and even identity as an individual organization within the city government. All these limitations are important and they need to be considered when designing an alternative or potential solution.

From the evidence in our case, it seems clear that there is potential in the characteristics of blockchain technologies to address some of the challenges and limitations of a centralized data sharing strategy. Particularly, the use of different types of chains and governance mechanisms can help to make blockchain useful for many more situations in which for example, there are actors with not enough computational power or storage capacity to be full members of the blockchain. Therefore, future research should explore more cases with specific needs and capabilities within the network of actors and determine if the results are still promising in those different situations. For instance, it could be the case that blockchain technologies can be fully implemented for certain city programs, but only partially useful for others, depending on the computational and storage limitations as well as the technical, managerial, and policy capabilities of the multiple agencies (and other city actors) involved in a specific public problem or policy domain.

Attention to context-specific conditions in public organizations – either social, technological, or policy related – is crucial to any technology implementation, especially when empirical evidence and theoretical studies on such endeavors are still scarce. Several constraints and incentives to the way public agencies adopt and implement novel technologies are at play. They help define limits and risks of using such technology in the light of its promises and the potential benefits to their operational routine and their overall outputs. Discussions in the public sphere should not ignore that, besides being an adoption issue, new processes will come with the implementation, and those processes are subject to institutional forces. Even if the use of blockchain technologies turns out to become a standard, there is no reason for researchers from multiple disciplines to not scrutinize it. At the intersection of social and political factors, implementing a pervasive and encompassing technology such as blockchain needs to take into account an extensive assessment of the state and the quality of inter-organizational relationships across agencies.

In addition, current discussions on the benefits of blockchain are predominantly technical and often operate under the assumption that stakeholders should buy into using blockchain technologies because of their benefits in terms of better information sharing and their unparalleled security standards. That perspective may overlook the fact that information sharing is a complex and multidimensional phenomenon, rarely addressed by technologies in isolation and top-down approaches. That assumption has also been made about the technical benefits of addressing data availability issues by opening data and making it accessible to multiple parties. However, it is clear now that for open data portals the technologies they are using have not resulted as critical for their success as having, for instance, guiding principles, information policies, and clear objectives on expected data uses. Something similar could happen with blockchain technologies and their study deserves to receive the same level of attention, particularly because security is not the only reason why people refrain from sharing information and making it more useful for decision making. Challenges in sharing information across organizations suggests that blockchain may help address important aspects such as integrating repositories, but issues with handling and managing data, or turning it into knowledge are likely to persist.

Finally, blockchain technologies could be used in very different ways and these combinations could affect the overall result in terms of security, privacy, and need for infrastructure. Based on our review of existing literature and the case of the city of Ningbo, we propose some implications and practical recommendations. For instance, it is very important to select the right blockchain governance mechanisms and in some cases several blockchains might be needed for a single situation. It seems that this could be the case for the city of Ningbo, but it is not clear whether this is applicable to other local governments in China or to other cities in a different country. Future research should study additional cases in China and elsewhere and test if the challenges we have identified are similar and if the recommendations could be applicable to other regional and national contexts.

Footnotes

Acknowledgments

This research was partially supported by the National Science Foundation of China (NSFC) under grant No. 91646127, the Outstanding Innovative Talents Cultivation Funded Programs 2017 of Renmin University of China and by the home institutions of the authors: Institute of Computing Technology-Chinese Academy of Sciences, University at Albany – State University of New York, Universidad de las Americas Puebla, Renming University, and Ningbo Academy of Smart City Development. The views and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of NSFC or their home institutions.

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