Data Management and Security in Blockchain Systems E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
Data Management and Security in Blockchain Systems offers a comprehensive exploration of how blockchain technology is reshaping the landscape of data management and security. This book addresses key aspects of blockchain-based systems, including data integrity, transparency, and tamper resistance, making it an essential resource for students, researchers, and professionals.
Covering topics from blockchain-enabled IoT traffic management to the integration of AI for enhanced security, this book presents solutions to current challenges such as cyberattacks, smart grid security, and scalable network designs. Each chapter is thoughtfully structured to provide readers with a solid understanding of blockchain applications in diverse domains. Perfect for those seeking to understand blockchain’s potential to secure and manage data in an increasingly interconnected world.
Key Features:
- Comprehensive overview of data management and security in blockchain networks.
- Practical insights into IoT, smart grids, and AI integration.
- In-depth analysis of cybersecurity challenges and solutions.
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Seitenzahl: 316
Veröffentlichungsjahr: 2024
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PREFACE
In an era of digital transformation, Blockchain technology, the Internet of Things (IoT), Artificial Intelligence (AI), and big data systems have ushered in a new paradigm of data management and security challenges. As organizations embrace the potential of these disruptive technologies to revolutionize various industries, the need for robust data management practices and stringent security measures becomes increasingly paramount.
This book delves into the intricate landscape of data management and security within blockchain-based systems, exploring the multifaceted dimensions of this evolving field. A comprehensive evaluation examines the fundamental principles and practices governing data management in Blockchain networks, addressing the complexities inherent in ensuring data integrity, confidentiality, and availability.
Central to this discourse is the symbiotic relationship between Blockchain technology and IoT, as they collaborate to fortify data security and streamline traffic management. By leveraging blockchain's immutable and decentralized nature, coupled with the connectivity and sensor capabilities of IoT devices, novel solutions emerge to mitigate security risks and optimize data handling processes.
Furthermore, this book elucidates the security challenges confronting Blockchain networks, elucidating the evolving threat landscape and vulnerabilities inherent in these decentralized systems. From cyber-attacks targeting big data repositories to the vulnerabilities plaguing IoT devices, each chapter dissects the intricacies of modern-day data security threats and proposes innovative solutions to fortify system resilience.
Moreover, it explores integrating AI-enabled security mechanisms within Blockchain systems, harnessing the power of machine learning and predictive analytics to identify and thwart potential threats proactively. Organizations can use this synergy to elevate their defense mechanisms, preemptively addressing security breaches and safeguarding critical data assets.
Finally, this book endeavors to serve as a comprehensive guide for practitioners, researchers, and policymakers grappling with data management and security complexities in the digital age. By offering insights into emerging trends, best practices, and technological advancements, it aims to empower stakeholders to navigate the intricate landscape of blockchain-based systems with confidence and resilience.
As we embark on this journey through data management and security in Blockchain networks, let us unravel the intricacies, confront the challenges, and embrace the transformative potential of these groundbreaking technologies.
List of Contributors
Evaluation of Data Management in Blockchain-based Systems
Bhakti Thakre1,*,Uma Yadav2Abstract
Blockchain records every data transaction on its network through a distributed digital ledger that is accessible to the public. The agreement-based process of recording and updating data across dispersed nodes is crucial for enabling trustless multi-party transactions in blockchain-based systems. The degree of utility and performance of a blockchain-based application is ultimately determined by understanding what and how the data is stored and changed. By offering an immutable and consistent data storage technology, it improves the quality of the data while posing new data management issues.
It analyzes blockchains from the viewpoint of a developer to highlight important concepts and considerations when incorporating a blockchain into a larger software system as a data store. Data Management involves architectural layers for storing data and conceptualizing each layer in blockchain, examining the flow of data in blockchain-based applications, andexploring data administration aspects for bloc-kchains. Data domination issues in blockchains are related to privacy and Quality Assurance (QA). The privacy of data can be preserved by keeping it in an encrypted form, but it affects usability and flexibility in terms of effective search. Attribute-based Searchable Encryption (ABSE) has proven its worth by providing fine-grained searching capabilities in the shared cloud storage.
In order to emphasize key ideas and things to keep in mind when integrating a blockchain as a data storage system into a larger software system, it analyzes blockchains from the perspective of a developer. Data management includes creating architectural layers for data storage, conceptualizing each layer in a blockchain, analyzing data flow in blockchain-based applications, and finally investigating data administration features for blockchains. The problems with data dominance in blockchains concern Quality Assurance (QA) and privacy. Data privacy can be maintained by encrypting it, but this compromises flexibility and usability in terms of efficient search. Since it allows for more precise searching in shared cloud storage, attribute-based searchable encryption, or ABSE, has shown its value.
The vulnerability of cloud services to assaults stems from their widespread accessibility. In cloud computing, data tampering is a risk to data integrity that can happen. Clients using cloud computing across a range of application areas demand assurances regarding the veracity and accuracy of their data.
INTRODUCTION
The potential for blockchain technology to revolutionize society has been likened to that of the WWW. The foundations of blockchain have been supported by a wide number of other applications in a short period of time, beyond the original purpose of cryptocurrency. These applications include asset management, insurance, finance, and medical/health. From the standpoint of these applications, blockchain enhances data quality by providing transparency, immutability, and consistency [1].
The architecture of block-chains, which provides these benefits, however, introduces current issues with data-management. As an ex, the following problems with blockchain as a network for data processing and storage may be identified as unresolved: Blockchain data formats include document and key-value store, which are frequently integrated with “off-chain” data storage. Therefore, ad hoc and handmade programming are needed in BC-based structures to search for and repossess a variety of data, unlike abstract and declarative query strategies in conventional databases. Sympathetically, how to get, assimilate, and analyze data in this assorted situation is crucial, especially in light of the growing need for large-scale blockchain data analytics.
The amount of information stored and managed by blockchain networks will only increase over time. However, many modern systems have low throughput, scalability, and latency. Moreover, open block-chains charge subscriptions for storing and updating information to deter idle data. Some of these issues can be resolved by carefully analyzing the on-chain and off-chain information structural adoptions completed by a block chain use.
Blockchain technology provides permanent and network-wide access to data storage. Concerns about quality and privacy, among other data governance issues, are brought up by this. Encrypting data is recommended, but doing so could leave it vulnerable to future brute-force decryption attacks (quantum computing advancements, for instance, could make current encryption techniques ineffective) or cause unintentional privacy breaches. The development of proper frameworks for blockchain data governance is therefore necessary in order to support effective management and responsible application of BC expertise.
It is imperative to investigate the usage of a digital ledge as an information storage medium in the framework of information organization, given these difficulties. Wherever there is a digital ledger, database managers and application developers can create and oversee a large software framework. In order to coexist with greater effectiveness, an auxiliary database must have a thorough understanding of blockchain technology, specifically with regard to data handling and storage. Errors, poor designs, and issues resulting from false presumptions about how block chains work should also be avoided. In other works, the functionality and distinctive characteristics of blockchain have been briefly compared to those of databases [2-5]. An addition to these efforts, we further construct the distinctions according to the way layers of software systems are commonly viewed by application developers.
The blockchain technology is methodically examined in this study from a database perspective. With the goal of improving the usefulness and appropriate usage of block chains in big software systems, we want to comprehend block chains as data storage better. To do this, we pinpoint and examine key data management challenges in the growth and administration of digital ledge-based systems. The subsequent contributions are:
Suggest a novel understanding of blockchain as the data repository for an application.Identify and assess the most effective approaches to operational problems and blockchain data structures.Examine the data management elements of block chains.Provide relevant, actionable insights into the rapidly developing fields of digital ledge analytics of data and reliable blockchain-based data examine.Examine the administration of blockchain records confidentiality and superiority, including existing problems and potential future possibilities.BLOCKCHAIN PERFORMANCE
Blockchains can offer a reliable and impartial data storage policy aimed at a sizable system that incorporates the technology. Trust and neutrality arise from the following features of the system, consensus mechanism, and cryptographic procedures it uses, along with the unique architecture of the ledger structure:
Transparency: Access to the information kept on the block-chain is available to all handlers inside the web. The data on open block-chain is thus reachable to everyone online.Immutability: As a result of the distributed consensus procedure, data cannot be changed or removed after it has been appended to the blockchain. Immutability, however, might only be probabilistic for block chains that employ specific consensus procedures. The blockchain network stores all transactions as immutable records. For regulatory purposes, these unchangeable recordings become a visible audit trail.Consistency: A fact is established across the block chain system toward distributed consensus and immutability, which makes sure that all committed data is accessible for all upcoming data manipulations.Equal Rights: Every member of the network has equal access to and control over the blockchain due to the disintermediation of the network. These privileges may vary depending on the consensus procedure used and may be influenced by the participant's stake or computing capacity.Availability: Every participant in the blockchain network is allowed to become an exact duplicate of the blockchain information. From the organization’s perspective, the information is accessible as long as there is at least one system in the blockchain network.From the standpoint of application engineering, each system plans choice involves a trade-off between a number of different properties. Likewise, the two main issues with the blockchain's design are confidentiality and performance. Each participant has access to all data on the blockchain, jeopardizing secrecy because the blockchain network does not contain any privileged users.Performance is the frequency at which connections are administered as well as the delay between accumulation and verifying fresh entries. The throughput limit is determined by the block-generation rate and block-size settings. Consensus protocol also influences the amount of time that passes between a transaction being submitted and committed. For Bitcoin, S. Nakamoto, this is roughly one hour (10 minutes) between blocks with six confirmations, and for Ethereum, it is roughly three minutes (15 seconds) between blocks with twelve confirmations.
DATA STORE USING BLOCKCHAIN TECHNOLOGY
As C. Pautasso [6] explains, we define a block-chain as the data-store level of a software system explained in Fig. (1). We show a standard database on the right to demonstrate our understanding of how a block-chain information store may be viewed from the predictable perspective of a DB-backed software design.
Fig. (1)) Database vs. blockchain application architectures.Generally speaking, blockchain technology is used at the core of three major application categories: exchange, agreements, and benefit. In conventional DB-backed applications, the abstract DM behind a block-chain application has to be translated to different levels of the data store in order for it to persist.
We describe our four-layer model for a blockchain data store in the parts that follow.To facilitate understanding, the layers are stratified, as illustrated in Fig. (1).
Layer of Logical Data
From the perspective of a DB designer, a conceptual model of the information is materialized into relational tables or another materialized form so that the request may relate to the information (for example, by sending queries to a database). In traditional database systems, it is clearly defined as part of software design, and a majority of program design constructs provision for this level in a standardized form.
Here, we look at how this idea helps in the blockchain technology block case. The topic for queries to access the data storage is covered distinctly. Here, we focus on “logical models” of applications that leverage blockchain technology. At this layer, the database developer can primarily see two constructs: assets and smart contracts.
Resources
Blockchains may be used to track the ownership of both traditional assets like stocks or titles and digitized traditional assets like cryptocurrencies. Since the purpose of tracking any type of data is beneficial other than ownership (such as the qualities and standing of a physical object), they are also known as states in many systems. Assets are represented on blockchains in two ways:
Un-spent TO, or UTXO, is an ability that represents the productivity of operation and is associated with an account. A single UT-XO can be used as input just once during a fresh operation. A UTXO-based model is used in QTUM2, R3-Corda1, and Bitcoin.
Each account's resource allocation is maintained separately in the account-balance model. The complete position of a blockchain net is represented by the balancing of everything.
Due to their statelessness, the UTXO architecture allows for simultaneous transactions and healthier confidentiality. Things get fragmented, which raises the difficulty of computing, storage, and programming. In contrast, because account-balance models are stateful, they offer an intangible representation of an account, so any transactions are made in addition to reduced complexity in programming, archiving, and processing. This architecture limits simultaneous operations and security.
Agreements with Intelligence
An Intelligence agreement is a group of instructions that can be carried out and are triggered by messages. When performing, these directives might modify the resources and produce fresh messages. A streamlined version of clever agreements can be included in an operation on first-generation blockchains like Bitcoin as an executable document. Smart contracts in 2nd group blockchains, such as Ethereum, facilitate data manipulation and storage on the blockchain. Unlike intelligent agreements, they ensure that any information they contain can only be changed by executing the processes that are stored in permitted databases. Due to this, smart contracts might be compared to “data with rules”.
Comments: An explanation, also known as a statement, is a specific situation or key to a digital asset, such as the owner of a Bitcoin UT-XO.
Due to this, the blockchain's sensible data store layer can be thought of as an essential attribute that maintains those records' versions and attributes or intelligent agreements. At this stage, this is comparable to how data is stored in a database without SQL. Depending on the blockchain technology, the value could be anything from a simple data structure or item to a JSON or XML text defining an asset or a smart contract. Typically, the key is an account. It is safe to conclude that scheme-less key values or records exist in the logical layer of a distributed ledger.
Document or key-value stores have been the go-to data storage, but in many cases, accurate data management in highly scalable systems still requires some degree of explicit modeling at this level. A few weaknesses in creating and building blockchain-based applications have been identified as the absence of suitable techniques to model information with guidelines [7]. To date, blockchain-based systems have been modeled using traditional modeling languages. For instance, UML CDs are used to model keen agreements since these systems typically utilize object-oriented programming languages, such as JavaScript in Hyperledger and Solidity in Ethereum [8]. The system's behaviour is defined using sequence diagrams, with roles specifically modeled in intelligent agreements.
Regarding enhancing the current modeling languages for blockchain [9] Lorikeet, A. B. Tran (2018), model-driven design equipment, expand BPMN to represent both the commercial method itself as a collection of keen agreements as well as smart contracts as data storage for smart contracts. Although these early studies serve as useful starting points, it is imperative to expand on them in order to include particular characteristics of blockchains.
The structure of the smart contract language places constraints on the, sophistication-reinforced facts, and the scope of data processing. Symbols are resources contained inside a smart contract, for instance. It is also possible to impose a user-defined schema on Ethereum and Hyperledger. While a J SON object provided as a resource might be mimicked like a collection of tables inside an intelligent agreement to get around blockchains' lack of schema, smart contracts mustn't be overly designed to the point where their cost competence and confidentiality are compromised.
Layer of Physical Data
Understanding various index structures, such as B Tree and H-tables, is extremely optimized for searching and accessing information, which is necessary from the traditional perspective of physical data storage. This section looks at the physical representations of blockchain data and how they affect reading and writing.
The data at this level can be divided into 3-Tier, as demonstrated in Fig. (1). A block is made up of a chosen subset of valid transactions, and blocks that comply with the consensus process are added to the ledger. Depending on the context, “transaction” in a blockchain might indicate several kinds of stuff. It could be used to describe both the processes that need to modify information stored on a blockchain and DS that records its operation's input constraints. Here, we refer to the data structure as a transaction record to distinguish it from the other two uses.
Transaction History
When “blockchain data operations” are carried out on the properties and for clever agreements, a transaction record stores both the inputs and outcomes. The most frequent procedures include opening fresh accounts, switching resources, and generating and implementing agreements.
A transaction record's permanent storage in the block after being selected for inclusion, which results in immutability, is a crucial component. The majority of blockchains also permanently retain unsuccessful transactions. This is due to the financial industry's influence on blockchain, where each record of data is a financial transaction needing the highest level of transparency.
A reverse transaction is the only option to undo any mistakes because the blockchain transaction records are unchangeable. Each deal record is uniquely identified and kept as a crucial assessment pair inside a chunk.
Chunk
Each chunk has a list of operations, although this list can be vacant if chunks are created on a regular basis. As a result, the precise structure and content of a block depend on the transaction data it includes and the blockchain implementation. For instance, a Merkle tree is used to build the operational data in a Bit-coin, whereas Trie is used in an Ethereum block [10]. A block can also keep track of other DS.
For instance, an Ethereum block uses another TRY to keep track of all asset and smart contract account balance pairs. In Hyperledger, the global state is tracked using a key-value store (such as LevelDB or CouchDB).
A trade-off between transaction throughput, interblock generation time, and block data replication speed affects the block size, which is a customizable parameter [11]. There are various ways to specify the block size. For instance, while Ethereum provides a computation limit (as a gas limit) for each block, BitCoin stipulates a data boundary.
Ledger
The term “blockchain” refers to an individual, universal list of blocks where every chunk is “chained” again to the previous chunk by including the hash of the data from the prior block. Such a chain of blocks can be seen in well-known systems like Bitcoin, Ethereum, and Hyperledger. As an alternative, Hashgraph8 employs a block-based D A G. Instead of using blocks, I.O.T.A 9's record uses a DAG of individual transactions.
Remarks: The three tiers described above are where the connections between the physical forms of blockchain data are made. As mentioned, the deployment of a specific blockchain platform determines the internal organization and data formats at various levels.
Blockchain information storage techniques, in divergence from traditional approaches, are limited and designed for storage rather than S-I, even though blocks are done differently. This is done in order to facilitate financial transactions, guarantee the distinctive qualities of blockchain, and lower the cost of data storage and transmission.
Most blockchain ledgers are fully replicated, which means that all of the resources, communications, and chunks are the same on every node on the net. Examples of these ledgers include those used by Ethereum and Bitcoin. Channel replicates to all nodes, in contrast to Hyperledger, which only replicates to nodes in a channel— a rational subgroup of blockchain network nodes that have permission to view each other's data. Since any changes made to the data on a small group of nodes cannot affect the data on different nodes before passing through the consensus process, such high replication levels promote immutability. However, unlike distributed databases, this type of replication doesn't lower latency or speed up operations.
This is due to the consensus mechanism, which aims to improve data consistency by ideally choosing one node to generate the next block and then duplicating it for all other nodes. Instead, blockchains can be used to improve throughput and latency over a number of systems. When sharding is implemented in Ethereum 2.0, a similar result is anticipated.
Data Access Layer
A P I -level access to the information storage in this section. The procedure of managing the CRUD is deep-rooted, as shown in Fig. (1). The traditional data access mechanism between the request and the information collection typically revolves around SQL (structured query language) statements to issue read and write operations.
Construct Then Modify
Only create and update operators are supported by transactions from the CRUD -perspective of information access. For instance, a transaction could transfer ownership of a property or debit and credit cryptocurrency accounts. A clever agreement can also be organized and start running using transactions. Some blockchains further distinguish between smart contracts and transactions used to manage accounts and assets; for example, Ethereum calls them “transactions and messages.”
Delete
To guarantee immutability, none of the blockchain implementations expressly offer the delete operator. An asset might be given a null value or its status changed to useless through a transaction, though. The relevant smart contract function can be called via a transaction to alter the resource formed. Although this could behave like a remove operator, all modifications remain tracked on the blockchain.
Read
Understanding information from a blockchain is more difficult than reading data from a database. Blockchain transactions, for instance, do not directly deliver results or show if the transaction was successfully completed because they use receipt-based temporary synchronous communication. While smart contract functions can query smart contract data, they also do not produce a response for the same reason. Similarly, we cannot submit seek readings from a blockchain. Instead, to obtain high-quality data elements passively, we need to employ specific identifiers, or IDs. “Block-chain explorer” denotes a tool for this type of querying. Through an application known as the blockchain client, a pioneer can connect to one or more systems that contain freshly created chunks.
The blockchain client searches the record sequentially, starting with the present chunk, for a stated resource, account, operation, or smart contract ID. In order to determine whether an operation has been recognized, refused, included, or completed, explicit querying is necessary.
Remarks: There are ongoing efforts to provide more effective information access to the application level, which is a crucial part of blockchain-based schemes. For instance, several blockchain explorers, like Etherscan-11, upload the blockchain data to a federal indexing server to facilitate faster and more complex queries. With the help of a specifically designed index, Hyperledger Fabric offers quick IDs and time-based querying of 1st class information components.
An SQL-like query language called Ethereum Query Language (EQL) was created with the goal of offering a general-purpose request and answer execution for blockchain data. Its basic language features, collections of chunks, kinds of items (such as transactions and accounts), and a BST enable queries to quickly retrieve information spread among several entries in the blockchain [12].
Libraries like Ethereum Web3 and Hyperledger Fabric-Network provide an asynchronous API to manage connections and access their results through the user interface, thereby hiding the complexity of their use. In contrast, Corda uses an RDBMS to store ledger data, supporting SQL read-write queries. BigchainDB is another approach that reads and writes blockchain data using a NoSQL query language.
Layer for Data Processing
In this part, we give a practical understanding of blockchains as data repositories. From a conceptual standpoint, we emphasize the data processing methods by which the blockchain ensures that information evenness and robustness, which are essential features of any data storage, are maintained. Concurrency control techniques, such as LBTM and recovery mechanisms, are similar ideas in conventional databases. However, compared to traditional databases, the objectives and results of the DP level in blockchain classifications are fundamentally different.
For instance, the data processing algorithms in a relational database are created to ensure that transactions have the ACID attributes. However, a blockchain's data operations are made to offer the transparency, immutability, and consistency that are unique to a blockchain. We think that being aware of these distinctions will help designers make wise choices when creating blockchain-based applications. As the main data operation/processing layer architecture, we will now go through how the consensus method affects the ACID aspects of blockchain transactions.
Table 1
