Introduction
Components of Blockchain
Blockchain technology utilizes fundamental cryptographic concepts such as hashing, asymmetric key cryptography, and digital signatures, along with essential principles of record keeping Key components of this technology include addresses, blocks, transactions, hashing, and digital signatures, each playing a crucial role in ensuring security and integrity within the blockchain system.
Hashing Methods
Hashing functions are a crucial element of blockchain technology, serving as a cryptographic mechanism that transforms data of any size into a unique, fixed-size output This output, represented as a fixed-size alphanumeric string, can originate from various inputs such as text, files, or images In the context of blockchain, hashing ensures data integrity by allowing users to verify that transmitted data remains unchanged; any minor alteration in the input will produce a different hash value The primary reason for employing hashing techniques in blockchain is their robust security features, including pre-image resistance, which makes these techniques uni-directional.
In cryptographic hash functions, it is impossible to derive the original input from the output, ensuring strong security Additionally, given a specific input, it is unfeasible to find a second input that produces the same output, known as second preimage resistance Furthermore, these functions are collision-resistant, meaning it is extremely difficult to identify two different inputs that yield the same hash output.
Most blockchain implementations utilize the Secure Hash Algorithm (SHA), specifically SHA-256, which generates a 256-bit output, typically represented as 64 hexadecimal characters In addition to SHA-256, other hashing algorithms like Keccak and RIPEMD-160 are also employed within blockchain networks These hashing techniques play a crucial role in various blockchain operations, including address creation, securing block data, and managing block headers.
A nonce, a random number utilized in the proof-of-work consensus mechanism, is combined with block data to generate varying hash outputs This model operates by adjusting the nonce value, allowing for the retrieval of specific output values while keeping the data constant The structure of a block is illustrated in Fig 1.1.
The block structure is divided into a header and a body, as illustrated in Figure 1.1 The body contains all the transactions within the block, while the block header includes essential elements such as the block's hash, the hash of the previous block, a nonce value, a timestamp indicating when the block was published, and the Merkle root, which is the consolidated hash of all transaction hashes.
Transactions
In blockchain, a transaction refers to the interaction between two entities, such as the transfer of cryptocurrencies like Bitcoin or the transfer of ownership of digital assets in a business context Each block in the blockchain may contain multiple transactions, which typically include transaction inputs, outputs, sender's address, sender's public key, and a digital signature While transactions are primarily associated with the transfer of digital objects, they can also facilitate data transfer, allowing users to publish data permanently on the blockchain or process data through smart contracts The validity and authenticity of a transaction are crucial; validity ensures adherence to blockchain protocols, while authenticity confirms that the sender possesses the digital assets being transmitted The sender digitally signs the transaction with a private key, which can be verified by others using the sender's public key.
Public Key Cryptography
Public key cryptography, also known as asymmetric key cryptography, plays a crucial role in blockchain operations This method employs a pair of keys: a public key, which is accessible to everyone, and a private key, which is kept confidential Importantly, the private key cannot be derived from the public key, ensuring a secure and reliable cryptographic system.
Asymmetric key cryptography allows for data encryption with a private key and decryption using a corresponding public key, ensuring data authenticity and integrity while maintaining transparency However, this method is slower in computation compared to symmetric key encryption, which uses a single key for both processes but requires user trust for key sharing To enhance efficiency, data is first encrypted using symmetric key techniques, and then the symmetric key itself is encrypted with asymmetric key methods, significantly improving the overall speed of the encryption process.
Address and Wallet
Addresses are short alphanumeric strings that serve as transaction points for senders and receivers in a blockchain network A hash function is utilized to generate a user's public key, and various blockchain implementations have distinct methods for deriving addresses To ensure security, users must store their private keys safely, often using software known as wallets These wallets not only store private keys but also manage users' addresses and public keys, allowing for the calculation of a user's digital asset ownership.
Blocks
Blockchain transactions are submitted by users through software applications, such as web services and mobile apps, and are sent to specific nodes for processing However, submitting a transaction does not immediately add it to the blockchain; instead, it enters a queue until the node publishes a block Each block consists of a block header containing metadata and a block body that includes all valid transactions The metadata varies depending on the blockchain implementation, and the blocks are linked through the hash of the previous block, forming a secure chain Any alteration in a block changes its hash, which is reflected in subsequent blocks, making it easy to detect tampering.
Consensus Mechanism
The core feature of blockchain technology is incentivizing users to publish blocks, with network nodes receiving cryptocurrency rewards for their contributions This creates competition among nodes to publish blocks, which is addressed through consensus mechanisms These mechanisms enable a group of users who may not trust one another to collaborate effectively Various consensus models are employed, including Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Elapsed Time (PoET).
The consensus mechanism is a decision-making process where network users collectively agree on actions that enhance the network To add a block to the blockchain, miners (nodes) must solve a complex cryptographic puzzle, which demands significant computational power Once a miner successfully solves the puzzle, the solution is broadcasted to the network for verification Upon successful verification, the new block is added to the blockchain, ensuring the integrity and continuity of the network.
Smart Contracts
In 1994, Nick Szabo introduced automated transaction procedures known as smart contracts, which execute contractual terms without the need for intermediaries These smart contracts consist of data and code, functioning through digitally signed transactions on a blockchain network Execution is carried out by network nodes, ensuring consistent results regardless of the number of nodes involved Smart contracts can perform various operations, including computations, data storage, and financial transaction reversals It's important to note that not all blockchain models support smart contracts; for example, the Bitcoin blockchain offers limited programmability but does not support them, while Ethereum and Hyperledger enable robust smart contract functionality The primary programming language for smart contracts is Solidity, with Serpent also being used but less commonly.
Evolution of Blockchain
Blockchain technology has significantly evolved beyond merely facilitating cryptocurrency decentralization Bitcoin represents the first generation of blockchain, while Ethereum and smart contracts characterize the second generation, leading to the emergence of decentralized applications (DApps) in the third generation The Bitcoin blockchain allows for decentralized financial transactions, removing the necessity for trusted intermediaries These transactions rely on public key cryptography and digital signatures, with nodes validating them through a proof-of-work mechanism that utilizes Hashcash and SHA.
The Bitcoin blockchain, while claiming user anonymity, allows for transaction tracing, making users pseudonymous Users are incentivized with bitcoins for publishing blocks, but scalability issues hinder its general-purpose application In 2013, Ethereum emerged as a versatile blockchain platform, addressing Bitcoin's scripting and transaction limitations This innovation led to the creation of smart contracts, which automate transaction validation and reduce costs associated with verification and fraud prevention, ensuring transparency However, Ethereum's smart contracts face challenges such as complex programming languages and difficulties in modification post-execution Despite these advantages, Ethereum struggles with high transaction volumes due to increasing economic demands Consequently, the blockchain is evolving towards a decentralized web, integrating data collection systems, smart contracts, communication networks, and open standards This evolution has facilitated the rise of Decentralized Applications (DApps), which operate on a blockchain backend while utilizing various programming languages for their user interfaces DApps are open source and leverage decentralized consensus mechanisms, gaining traction in various industries and enabling cross-chain communication to meet the demands of Industry 4.0.
Applications of Blockchain
Blockchain technology extends beyond decentralized cryptocurrencies, revolutionizing business transaction models and asset management protocols Its applications are diverse, impacting areas such as e-voting, car rentals, and entertainment services, while significantly influencing major sectors like FinTech.
[20], Healthcare [21], Governance, Supply chain [22], Manufacturing Industries, Insurance, Education, IoT [23], Big Data systems and Machine Learning [24] etc.
Blockchain technology is revolutionizing financial services by facilitating secure financial transactions and efficient asset management without the need for trusted third parties, resulting in faster and more reliable services In the insurance sector, blockchain enhances the detection of fraudulent claims and abandoned policies, creating a transparent and risk-free environment Additionally, the encryption features of blockchain allow insurers to maintain ownership records of insured assets, further improving the industry's integrity and efficiency.
The Internet of Things (IoT) encompasses any object connected to the Internet, with significant applications in areas like smart homes, smart cities, and cloud integration In healthcare, blockchain technology can decentralize vast amounts of data generated from patient monitoring, clinical research, and medical record storage, allowing patients, doctors, and insurance companies to manage records securely In the education sector, blockchain is still emerging, with potential uses in identity management and digital certificates, enabling users to share and verify academic achievements Additionally, blockchain enhances e-voting by providing a secure and tamper-proof method for casting votes, thus preventing fraud Furthermore, the supply chain sector stands to benefit greatly from blockchain, streamlining processes such as goods transmission, item traceability, customer refunds for faulty deliveries, and reducing transaction costs.
Blockchain technology is transforming various sectors beyond those previously mentioned, with researchers actively exploring additional applications to harness its full potential.
Challenges of Blockchain
Scalability
As transaction volumes continue to rise, the size and complexity of blockchain networks are also increasing Each node is responsible for collecting and validating all transactions on the blockchain Additionally, there are limitations regarding block size and the time required to publish new blocks, which can impact overall performance and efficiency.
The current blockchain system processes only seven transactions per second, which is insufficient for handling large volumes of real-time data Additionally, the small block size leads miners to prioritize transactions with higher fees, causing delays for smaller transactions To address these challenges, advancements in storage optimization and blockchain redesign are being explored.
Loss of Privacy
Blockchain technology utilizes public key cryptography to enhance user privacy by keeping identities anonymous during transactions However, complete transactional anonymity is not guaranteed, as the identities associated with transactions and the balances linked to each cryptographic key are publicly available This transparency allows for the potential identification of users through the monitoring of their transaction activities.
Selfish Mining
Blockchain technology is vulnerable to various attacks, including Selfish Mining, where a miner withholds their mined blocks from the public until certain conditions are met This results in the creation of a longer private chain that, when revealed, can lead other miners to abandon the openly available chain, wasting their resources Consequently, selfish miners can gain higher rewards at the expense of honest miners Additionally, blockchain systems are susceptible to other threats such as Sybil attacks, Double Spending, and 51% attacks.
Blockchain is revolutionizing both industry and academia through its unique features such as decentralization, anonymity, integrity, and transparency Its applications extend far beyond cryptocurrencies and financial transactions, with its decentralized nature offering enhanced data redundancy and survivability compared to traditional internet systems As a reliable solution for trust-related issues, blockchain is increasingly being adopted across various global sectors, despite not yet reaching full maturity.
1 Dai, W.: B-Money [Online] (1998) Available: https://www.weidai.com/bmoney.txt
2 Szabo, N.: Bit Gold [Online] (2005) Available: https://unenumerated.blogspot.de/2005/12/bit- gold.html
3 Finney, H.: Rpow [Online] (2004) Available: https://cryptome.org/rpow.htm
4 Lamport, L., Shostak, R., Pease, M.: The Byzantine generals problem ACM Trans Program. Lang Syst 4(3), 382–401 (1982)
5 Douceur, J.: The Sybil attack In: Proceedings of 1st International Workshop Peer Peer Systems, pp 251–260, March 2002
6 Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system Tech Rep (2008) [Online]. Available: https://bitcoin.org/bitcoin.pdf
7 Merkle, R.C.: A digital signature based on a conventional encryption function In: Pomerance,
C (ed.) Advances in Cryptology—CRYPTO ’87: Conference on the Theory and Applications of Cryptographic Techniques, Santa Barbara, CA, Aug 1987, pp 369–378
8 Narayanan, A., Bonneau, J., Felten, E., Miller, A., Goldfede, S.: Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction Princeton University Press (2016)
9 Kosba, A., Miller, A., Shi, E., Wen, Z., Papamanthou, C.: Hawk: the blockchain model of cryptography and privacy-preserving smart contracts In: 2016 IEEE Symposium on Security and Privacy (SP), San Jose, CA, May 2016, pp 839–858
10 Tschorsch, F., Scheuermann, B.: Bitcoin and beyond: a technical survey on decentralized digital currencies IEEE Commun Surv Tutor 18(3), 2084–2123, 3rd Quart (2016)
11 Bitcoin Block Explorer Accessed: 13 June 2017 [Online] Available: https://blockexplorer. com/blocks-date/
12 Szabo, N.: Smart Contracts (1994) https://www.fon.hum.uva.nl/rob/Courses/InformationInSp eech/CDROM/Literature/LOTwinterschool2006/szabo.best.vwh.net/smart.contracts.html
13 Wood, G.: Ethereum: A Secure Decentralised Generalised Transaction Ledger Accessed: 19 Nov 2016 [Online] Available: https://paper.gavwood.com
14 Dhillon, V., Metcalf, D., Hooper, M.: The hyperledger project In: Blockchain Enabled Applications, pp 139–149 (2017)
15 Buterin, V.: Ethereum white paper: a next generation smart contract & decentralized application platform (2013) Available at: https://www.theblockchain.com/docs/Ethereum_white_papera_ next_generation_smart_contract_and_decentralized_application_platfor
16 Raval, S.: Decentralized Applications : Harnessing Bitcoin’s Blockchain Technology, 1st edn. O’Reilly Media (2016)
17 Bogner, A., Chanson, M., Meeuw, A.: A decentralised sharing app running a smart contract on the ethereum blockchain In: ACM International Conference Proceeding Series, pp 177–178 (2016)
18 Unibright—The Unified Framework For Blockchain based Business Integration (2018) https:// unibright.io Accessed on Nov 2018; Accenture: Banking on blockchain A value analysis for investment banks Report (2017)
19 Boucher, P.: What if blockchain technology revolutionised voting? Scientific Foresight Unit (STOA) European Parliamentary Research Service (2016)
20 Conti, M., Sandeep Kumar, E., Lal, C., Ruj, S.: A Survey on security and privacy issues of bitcoin IEEE Commun Surv Tutor 20(4), 3416–3452, Fourth quarter 2018
21 Angraal, S., Krumholz, H.M., Schulz, W.L.: Blockchain technology: applications in health care Circul Cardiovascular Qual Outcomes 10(9) (2017)
22 Ahmed, S., Broek, N.T.: Food supply: blockchain could boost food security Nature 550(7674),
23 Boudguiga, A., Bouzerna, N., Granboulan, L., Olivereau, A., Quesnel, F., Roger, A., Sirdey,R.: Towards better availability and accountability for IoT updates by means of a blockchain.In: Proceedings—2nd IEEE European Symposium on Security and Privacy Workshops, EuroS and PW 2017, pp 50–58 (2017)
24 Abdullah, N., Hồkansson, A., Moradian, E.: Blockchain based approach to enhance big data authentication in distributed environment In: International Conference on Ubiquitous and Future Networks ICUFN, pp 887–892 (2017)
25 Ali, M., Miraz, M.H.: Cloud computing applications In: Proceedings of the International Conference on Cloud Computing and eGovernance—ICCCEG 2013, Internet City, Dubai, United Arab Emirates, pp 1–8 (2013) Available: https://www.edlib.asdf.res.in/2013/iccceg/ paper001.pdf
26 Gartner: Top Trends in the Gartner Hype Cycle for Emerging Technologies Gartner, Inc., Gartner Hype Cycle 2017, August 2017 Available: https://www.gartner.com/smarterwithgart ner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
27 Tapscott, D., Tapscott, A., Revolution, B.: How the Technology Behind Bitcoin Is ChangingMoney, Business, and the World, 1st edn Penguin Publishing Group, New York, USA (2016)
Rohit Saxena, Deepak Arora, Vishal Nagar, and Satyasundara Mahapatra
Bitcoin, introduced by Satoshi Nakamoto in 2009, is the largest and most widely accepted cryptocurrency, operating on a decentralized blockchain ledger It functions as a peer-to-peer (P2P) network, enabling direct electronic payments between parties without intermediaries Transactions are verified by miners, who maintain the blockchain and ensure security through a Proof-of-Work (PoW) mechanism that prevents double-spending The immutable nature of Bitcoin transactions, coupled with user anonymity, contributes to its popularity Various factors, including market capitalization, marketplace dynamics, and miners' revenue, influence Bitcoin's price fluctuations This chapter explores these factors and compares them with other cryptocurrencies.
Keywords Bitcoinã Blockchain ãCryptocurrencyã Consensus ã Miners
Pranveer Singh Institute of Technology, Kanpur, India
Amity University, Lucknow, India e-mail: darora@lko.amity.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
S K Panda et al (eds.), Blockchain Technology: Applications and Challenges,
Intelligent Systems Reference Library 203, https://doi.org/10.1007/978-3-030-69395-4_2
Introduction
Bitcoin operates through a network of interconnected computing nodes where its source code is stored on the blockchain Each collection of transactions forms a block, and the blockchain itself is a series of these blocks All nodes maintain the same set of blocks and transactions, allowing them to transparently observe new blocks being added A theoretical threat known as the 51% attack could occur if miners gain control of over 51% of the hash rate, but this scenario is highly unlikely due to the current network comprising over 10,000 computing nodes, which continues to grow.
Blockchain technology is transforming the information technology industry by providing enhanced security, efficiency, and flexibility Key applications of blockchain include cryptocurrencies like Bitcoin, Ethereum, Litecoin, and Ripple Bitcoin, a leading cryptocurrency, facilitates peer-to-peer decentralized payments without intermediaries, with transactions verified by network nodes and recorded on the public Bitcoin Blockchain Introduced by the enigmatic Satoshi Nakamoto in 2008 and launched as open-source software in 2009, Bitcoin has emerged as the most popular cryptocurrency, significantly boosting the economy within a few years By utilizing a P2P network, Bitcoin operates independently of banks and other financial institutions, allowing for seamless transaction validation Its revolutionary features have attracted growing public interest, making it a fast, tax-free, and convenient digital currency.
Bitcoin operates as a cryptocurrency based on account entries, where the surplus is reflected in individual bitcoin accounts These accounts utilize Elliptical Curve Cryptographic Key Pairs, employing the Elliptical Curve Digital Signature Algorithm (ECDSA) to ensure that electronic funds are only spent by their legitimate owners ECDSA relies on the secp256k1 curve specification, which features 256-bit private keys, and is integral to the asymmetric key cryptography used in the Bitcoin blockchain The secp256k1 parameters are crucial for maintaining secure and competent cryptographic standards within the Bitcoin network.
A private key is a randomly generated 256-bit secret number known only to the entity that created it In the Bitcoin blockchain, users with access to their private key can spend their funds securely.
A public key is a number derived from a private key, which remains confidential It plays a crucial role in verifying the authenticity of a signature, ensuring it was created using the correct key while the private key stays secure In the Bitcoin blockchain, public keys can be categorized as either uncompressed, consisting of 65 bytes prefixed with 0×04 and followed by two 256-bit keys, or compressed, which are 33 bytes long and prefixed with either 0×02 or 0×03, also with a key length of 256 bits.
(c) Signature: number, generated mathematically using a hash of something that is to be signed, plus a private-key.
Bitcoin addresses are globally identifiable and are created using a public key that operates through a simplex, unidirectional function This allows users to transfer bitcoins to specific addresses To spend these funds electronically, the sender must possess the corresponding private key Bitcoin users can generate multiple addresses by utilizing cryptographic software or bitcoin wallets.
Bitcoin Block’s Structure
Bitcoin Transactions’ Structure
Transactions are the foundation of the Bitcoin system, functioning as data structures that facilitate the transfer of funds from an input source to an output destination Each transaction must be created, validated, propagated, and added to the public ledger, known as the blockchain Various fields within the transaction play a critical role in this process, as outlined in Table 2.2.
Unspent Transaction Outputs (UTXO) are fundamental to Bitcoin transactions, serving as locked units of currency assigned to specific owners within the network The Bitcoin network meticulously tracks available UTXOs, which are recorded on the blockchain whenever a user receives Bitcoin Wallet applications determine a user's balance by scanning the blockchain and aggregating all associated UTXOs UTXOs can be valued in satoshis, with Bitcoin divisible into eight decimal places, unlike dollars which are divided into two Once a UTXO is created, it cannot be split; thus, if a larger UTXO is needed for a transaction, it must be fully utilized, resulting in outputs that reflect the transaction's value For instance, if a user has a 30 Bitcoin UTXO but only needs to send 2 Bitcoin, the transaction will consume the entire 30 Bitcoin UTXO and generate two outputs: a payment of 2 Bitcoin to the recipient and a change output reflecting the remaining balance.
Table 2.2 Schematic structure of bitcoin transaction
Field Description Size (in bytes)
Version Present the rules to be followed by transaction 4
Input counter Total inputs included 1 to 9 (VarInt)
Inputs Transaction input (One or more) Varying size
Output counter Total outputs included 1 to 9 (VarInt)
Outputs Transaction output (One or more) Varying size
Locktime Block number/Unix timestamp 4 and (b) payment of 28 bitcoin as the change back to the wallet which is at hand for the transactions to come.
In the Bitcoin network, UTXOs (Unspent Transaction Outputs) are utilized in transactions where exhausted UTXOs serve as transaction inputs, while newly created UTXOs are referred to as transaction outputs This process facilitates the transfer of Bitcoin value from one owner to another, resulting in a chain of transactions that consume existing UTXOs and generate new ones Each transaction requires the current user's signature to unlock and utilize the UTXO, subsequently creating new UTXOs that are secured to the next owner's Bitcoin address.
Transaction outputs are recorded on the Bitcoin ledger, forming spendable clusters known as Unspent Transaction Outputs (UTXOs) The entire network recognizes these UTXOs, making them available for future transactions When a user transacts Bitcoin, a UTXO is created and registered to the owner's address, allowing for spending Bitcoin clients track UTXOs in a database referred to as the UTXO pool or UTXO set Each transaction output consists of two essential components.
The total amount of bitcoins is measured in satoshis, which represent the smallest unit of the cryptocurrency Additionally, the locking script, referred to as "encumbrance," secures the bitcoin amount by specifying the conditions that must be met to spend the output.
In the Bitcoin blockchain, transaction inputs refer to the pointers of Unspent Transaction Outputs (UTXOs), which include the transaction hash and sequence number of a UTXO These inputs also contain scripts necessary for unlocking and spending UTXOs, which must fulfill the conditions set by the corresponding locking scripts A signature is required to validate ownership of the Bitcoin address linked to the locking script When a user initiates a transaction, their wallet selects from available UTXOs to create the payment; for example, to make a payment of 0.020 Bitcoin, the wallet may combine two UTXOs of 0.010 Bitcoin each After selecting the appropriate UTXOs, the wallet generates unlocking scripts that meet the locking script requirements, incorporating the necessary signatures for each UTXO Finally, the wallet compiles these unlocking scripts and UTXO references to form the complete transaction input.
In blockchain technology, mining refers to the process of adding a new block to the end of the chain Within the Bitcoin network, this mining process results in the creation of new bitcoins, thereby increasing the electronic currency supply Specialized nodes, known as mining nodes, play a crucial role in this network by monitoring for new blocks that are propagated throughout the Bitcoin ecosystem.
The bitcoin network is safeguarded against dishonest transactions and double-spending through the essential role of miners, who validate and document transactions on a distributed ledger Every 10 minutes, miners create a new block containing the most recent transactions, which are then added to the blockchain as confirmed transactions In return for their processing power, miners receive rewards, allowing bitcoin holders to spend their earnings securely.
Mining nodes engage in a competitive process to solve complex cryptographic hash puzzles, earning rewards in the form of newly generated coins and transaction fees This process, known as Proof-of-Work (PoW), is essential for Bitcoin's security model Mining not only supports the cryptocurrency's monetary supply but also resembles how banks create new money through currency issuance Additionally, the rate at which new bitcoins are generated decreases approximately every four years, or every 210,000 blocks, impacting the overall supply.
50 in January 2009 which declined to 6.25 bitcoin every block on May 11, 2020 [6].
In this manner, there is an exponential decrease in the reward of the miner and until
2140 approximately all the bitcoin i.e 20.99999998 million will be issued and no new bitcoins will be issued.
Every Bitcoin transaction incurs a fee, which represents the difference between the inputs and outputs of the transaction This fee is awarded to the miner who successfully completes the Proof of Work (PoW) challenge Over time, as miner rewards decrease and the number of transactions per block rises, a larger share of miners' earnings will come from transaction fees The mining process not only ensures network-wide consensus in a decentralized environment but also protects the Bitcoin network from potential attacks.
Traditional payment systems rely on a centralized authority to verify and clear transactions, functioning through a trust model In contrast, the Bitcoin blockchain operates without a central authority, with blocks independently assembled across the network, creating a public ledger that serves as a reliable record The decentralized consensus of Bitcoin is achieved through the interaction of four distinct processes occurring on the mining nodes within the network.
(a) Every transaction is verified independently based on an extensive criteria list. The verification is done by the full node.
Mining nodes independently aggregate transactions into new blocks using the Proof of Work (PoW) algorithm, which requires demonstrated computational effort Each node verifies and assembles the latest blocks into the blockchain, ensuring accuracy and integrity Ultimately, nodes select the chain with the highest cumulative calculations demonstrated by PoW, reinforcing the decentralized nature of the network.
Wallet software facilitates transactions by gathering Unspent Transaction Outputs (UTXOs), providing the necessary scripts for unlocking them, and generating new outputs for the intended recipients Once created, the transaction is sent to neighboring nodes for distribution across the network Each node is responsible for verifying the transaction, ensuring that only valid ones are propagated further while invalid transactions are immediately discarded by the first node that detects them This verification process is crucial for maintaining the integrity of the entire network.
Bitcoin’s Anonymity & Privacy
Anonymity and privacy are often confused, yet they represent distinct concepts: anonymity conceals the identity of the user, while privacy safeguards personal information In practical terms, users prioritize privacy over anonymity since protecting personal data is essential for its proper use For instance, while many may have access to a user's email account information, only the account owner can access its sensitive content through a password Privacy is vital across various systems and applications Conversely, anonymity is often sought by criminals, making it challenging to hold them accountable for their actions However, there are legitimate applications for anonymity, such as in voting systems, where being untraceable and unidentifiable is crucial.
True anonymity is difficult to achieve, as many applications that promise it have vulnerabilities that can expose user identity Mixing services, or mixnets, are utilized to prevent message tracing within networks, but they may be unreliable and incur additional computational and communication costs Onion routing is a widely used method for anonymization, effectively addressing IP tracking issues However, even TOR, the most prominent and successful anonymity network, has its own flaws.
Maintaining anonymity and privacy requires significant resources, including time, space, and computational power Users may also incur additional costs to protect their privacy A notable incident in Turkey illustrates this risk, where a passenger using local mobile apps was assaulted by a cab driver who felt disrespected The driver, seeking revenge, tracked the passenger for two days, highlighting the dangers that arise when anonymity and privacy are compromised.
Fundamentally, for achieving deanonymization and extracting the information, analysis of privacy and anonymity is performed by the spending effort that would weaken the privacy of the users.
After analysis, outcomes are the potential aims to be achieved Outcomes of analyzing privacy and anonymity are as follows:
(a) Bitcoin Addresses Discovery : All the possible bitcoin addresses of an entity are discovered including the name of the person or the company.
(b) Identity Discovery : All the potential distinguishing information, for instance, the name of the company or the person is procured that starts with a bitcoin address.
(c) Mapping of IP Address with Bitcoin Address : Mapping of possible IP
Addresses where the transaction was generated is done with the Bitcoin addresses.
Bitcoin users are often provided with new addresses for each transaction, leading to the creation of multiple addresses per user This practice results in the linking of users' addresses, which can have implications for privacy and security in the cryptocurrency ecosystem.
(e) Mapping of Geo-locations with Bitcoin Address : Using the bitcoin address geographical location of the user can be obtained.
The outcomes mentioned earlier may lead to a transition where a user's Bitcoin address can be uncovered and connected to their other Bitcoin addresses This mapping process facilitates the identification of the user and can reveal their geographic location associated with that address.
Research indicates that there are multiple approaches to achieving this objective While some studies actively implement these methods, others merely reference them without application Below are the studies that have either cited or utilized these methods in their research.
Transacting in Bitcoin requires buyers to know the seller's bitcoin address, which must be provided for payment to occur This facilitates the discovery of a user's bitcoin address, especially in sales contexts Active participation in the network is essential for transactions, as noted by Reid and Harrigan, who also highlighted its association with money laundering activities Meiklejohn et al referred to this transacting method as a re-identification attack, where accounts are created and purchases are made from notorious Bitcoin merchants and service providers like Mt Gox and Silk Road.
Leveraging publicly available off-network data sources can help identify Bitcoin addresses associated with specific user entities Websites that facilitate donations play a crucial role in uncovering these connections.
Reid and Harrigan utilized IP and key information to identify entities involved in the theft of 25,000 BTC, employing off-network data for their analysis Ortega gathered approximately 4,000 Bitcoin addresses from a prominent online forum, where users often disclose their real-world locations He developed scripts to associate these Bitcoin addresses with user identities, linking 1,825 distinct users to the 4,000 addresses, despite some users having multiple addresses Additionally, Meiklejohn et al collected over 4,500 Bitcoin addresses from blockchain.info, a platform that aggregates addresses from user signatures in forums like Bitcointalk, noting that this method is less conclusive compared to direct transaction analysis.
[22] inspected that the bitcoin addresses from the forum bitcointalk signatures and tried for identifying around 2,320 users with a 2,404 address in less than
Spagnuolo et al introduced the BitIodine framework for open blockchain analysis, leveraging data from platforms like Bitcointalk and the bitcoin-OTC market, as well as insights from Casascius on physical currency and feedback from notorious scammers to identify problematic bitcoin users Additionally, they drew information from BitFunder, a closed stock exchange Biryukov et al utilized Bitnodes to compile a list of active bitcoin servers, assessing the likelihood of entry nodes going offline Lische and Fabian collected over 223,000 unique IP addresses from ipinfo.io, linked to approximately 15.8 million transactions, and explored other sources like torstatus.blutmagie.de for further IP address data.
Machine Learning Approaches to Price Prediction
Bitcoin is recognized as a monetary asset traded on various exchanges, akin to the stock market Researchers have explored numerous factors influencing Bitcoin's price fluctuations through diverse investigative approaches Studies by authors [25–27] exemplify this research With advancements in artificial intelligence, several machine learning (ML) and deep learning (DL) models have been proposed for predicting Bitcoin prices [27–33] Notably, Chen et al [34] developed a forecasting model, which was later implemented by Shah et al [35] Shah's model achieved an impressive 89% return over 50 days, with a Sharpe ratio that assesses performance while considering risk, and demonstrated a 33% improvement over a buy-and-hold strategy during the testing period Despite several attempts, independent replication of this study has been unsuccessful Additionally, Geourgoula et al [36] conducted sentiment analysis using Support Vector Machine (SVM) to investigate Bitcoin price determinants, while Matta et al [37] examined the correlation between Bitcoin's price, Google Trends, and tweets, finding a weak to moderate correlation with positive tweets on Twitter.
Recent studies have explored various predictive techniques for Bitcoin price forecasting, with Google Trends data identified as a significant predictor However, limitations exist, such as short data collection periods, like the 60 days used in some research Matta et al found a strong correlation between Google Trends views and Bitcoin prices, while Kristoufek's wavelet coherence analysis indicated a positive relationship between search engine views, network hash rate, and mining difficulty with Bitcoin prices Greaves et al achieved a 55% accuracy using Artificial Neural Networks (ANN) and Support Vector Machines (SVM), highlighting constraints in blockchain data forecasting due to external exchange influences Conversely, Madan et al reported over 97% accuracy with machine learning techniques, although concerns about overfitting were noted McNally et al compared long short-term memory (LSTM) and recurrent neural network (RNN) models against the traditional ARIMA model, demonstrating that LSTM and RNN outperformed ARIMA Saad and Mohaisen integrated blockchain attributes with price information to enhance forecasting models, while Jang and Lee incorporated macroeconomic factors alongside blockchain data, with Jang et al later showcasing the superiority of LSTM models Additionally, Shintate and Pichl introduced a deep learning-based random sampling model that outperformed LSTM-based approaches.
Threats and Machine Learning Based Solution
Network infrastructure has been in existence for decades, alongside malicious users who engage in fraudulent activities within the system These malignant actors conduct deceitful transactions, particularly in financial exchanges, posing a significant threat to the integrity of network operations It is essential to identify and mitigate suspicious behavior in the Bitcoin network due to the rapid increase in fraud cases One common threat is the double-spending attack, which occurs when a client attempts to use the same Bitcoin for multiple transactions, often resulting from delays in broadcasting pending payments Recent research has introduced various solutions for anomaly detection, including machine learning techniques For example, Smith et al employed clustering methods to detect malicious activities and differentiate between legitimate and harmful users in the network.
Numerous studies have employed machine learning (ML) techniques to tackle security threats in the Bitcoin network Pham et al investigated disreputable users and transactions using unsupervised learning methods, such as Mahalanobis distance, Unsupervised SV Machine, and k-means clustering, on Bitcoin-generated graphs They further advanced their research by detecting anomalies in the Bitcoin system through client and transaction analysis, identifying destructive behavior as a proxy for dubious activities Monamo et al utilized kd-trees and trimmed k-means for fraud detection within the Bitcoin blockchain, asserting that their approach identified more fraudulent transactions compared to similar studies Additionally, Zambre et al focused on identifying potential rogue users based on real reported robberies using k-means classification, contributing to the ongoing efforts to enhance security in the Bitcoin ecosystem.
Recent studies have focused on the automated detection of fraudulent schemes in cryptocurrency and mobile transactions Notably, [49] introduced a method for identifying Ponzi schemes in Bitcoin using supervised learning algorithms, while Zhdanova et al [50] developed a strategy to uncover fraud chains in Mobile Money Transfer through machine learning-based micro structuring techniques These advancements highlight the growing importance of machine learning in combating financial fraud.
Recent studies have introduced innovative machine learning (ML) approaches to enhance the understanding of Bitcoin's anonymity and cybercriminal activities [51] pioneered a method using supervised ML to predict undetected entities, while Yin et al [52] analyzed the Bitcoin ecosystem, classifying 854 observations into 12 categories, identifying 5 related to cybercrime among approximately 100,000 unclassified cases Hirshman et al [53] utilized unsupervised ML algorithms to explore anonymity in Bitcoin transactions through dataset clustering Liu et al [54] focused on detecting double-spending attacks using an ML framework involving immune-based blockchain nodes for identification Additionally, Bogner et al [55] employed machine learning for graphical threat detection, helping human operators visualize blockchain features Lastly, Remy et al [56] monitored client activities within the Bitcoin ecosystem by applying community identification techniques on low-intensity network signals through machine learning analysis.
Recent advancements in blockchain technology and machine learning have led to innovative solutions for enhancing security and efficiency within cryptocurrency ecosystems A proposed by-product protocol utilizes smart contracts and machine learning for crowd-sourcing research funds without intermediaries Additionally, a machine learning approach has been developed to analyze ransomware datasets, offering a layered defense against cryptographic threats in Bitcoin and other cryptocurrencies Empirical studies have employed clustering stress tests to identify spam transactions in the Bitcoin network Furthermore, a novel identification scheme combining natural language processing and machine learning focuses on recognizing phishing rings through the monitoring of newly registered domains Off-chain knowledge solutions have also been introduced to separate and categorize Bitcoin addresses, thereby minimizing user input errors and improving reliability Lastly, intelligent software agents have been deployed to monitor stakeholder activities and detect anomalies in the Bitcoin ecosystem using advanced machine learning algorithms and game theory principles.
A machine learning-based classifier has been developed to distinguish between advertisements posted by the same author and those from various other authors This system incorporates a linking technique that leverages information from Bitcoin transactions and public wallets, specifically focusing on connections to sex advertisements.
Conclusion
This chapter introduces Bitcoin and the cryptographic mechanism ECDSA used in it.
The structure of a Bitcoin block comprises key components such as block size, block header, transaction counter, and a list of transactions Each Bitcoin transaction includes fields for version, total inputs, outputs, and locktime The mining process plays a vital role in adding new Bitcoins to the electronic fund supply, where mining nodes compete to solve complex cryptographic puzzles and earn transaction fees as rewards Transaction verification follows a strict checklist to ensure data structure, syntax, input/output lists, and size limitations are met Machine learning, particularly LSTM, is effective for forecasting Bitcoin prices, outperforming models like Deep Neural Networks and SVM Anonymity and privacy are essential for secure transactions on the Bitcoin network, while common security threats and their behaviors are addressed through machine learning solutions.
The future of bitcoin may see advancements in deanonymization to combat illicit activities such as robbery and ransomware Additionally, machine learning (ML) and deep learning (DL) techniques can be employed to predict bitcoin prices and identify potential threats within the bitcoin network.
1 Dhulavvagol, P., Bhajantri, V., Totad, S.: Blockchain ethereum clients performance analysis considering e-voting application Procedia Comput Sci 167 2506–2515 (2020) https://doi. org/10.1016/j.procs.2020.03.303
2 Rahouti, M., Xiong, K., Ghani, N.: Bitcoin concepts, threats, and machine-learning security solutions IEEE Access, 1–1 (2018) https://doi.org/10.1109/ACCESS.2018.2874539
3 Herrera-Joancomartí, J.: Research and Challenges on Bitcoin Anonymity 8872 https://doi. org/10.1007/978-3-319-17016-9_1 (2014)
5 Yaga, D., Mell, P., Roby, N., Scarfone, K.: Blockchain Technology Overview (2019)
6 Xiao, Z., Xiao, Y.: Security and privacy in cloud computing IEEE Commun Surv Tutor 15,
7 Bradbury, D.: Anonymity and privacy: a guide for the perplexed Network Security (2014). https://doi.org/10.1016/S1353-4858(14)70102-3
8 Eckhoff, D., Wagner, I.: Privacy in the smart city—applications, technologies, challenges and solutions IEEE Commun Surv Tutor., 1–1 (2019) https://doi.org/10.1109/COMST.2017.274 8998
9 Ferrag, M.A., Maglaras, L., Ahmim: A privacy-preserving schemes for ad hoc social networks: a survey IEEE Commun Surv Tutor., 1–1 (2017) https://doi.org/10.1109/COMST.2017.271 8178
10 Davenport, D.: Anonymity on the internet: why the price may be too high Commun ACM 45
11 Kelly, D., Raines, R., Baldwin, R., Grimaila, M., Mullins, B.: Exploring extant and emerging issues in anonymous networks: a taxonomy and survey of protocols and metrics IEEE Commun Surv Tutor 14 1–28.https://doi.org/10.1109/SURV.2011.042011.00080
12 Chaum, D.: Untraceable electronic mail, return addresses and digital pseudonyms Commun. ACM 24, 84–88 (1981) https://doi.org/10.1145/358549.358563
13 Chaum, D.: cMix: anonymization by high-performance scalable mixing IACR Cryptol ePrint Archive, Rep 2016/008 (2016)
14 Syverson, P., Goldschlag, D., Reed, M.: Anonymous connections and onion routing In: Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, pp 44–54 (1997) https://doi.org/10.1109/SECPRI.1997.601314
15 Dingledine, R., Mathewson, N., Syverson, P.: Tor: the secondgeneration onion router In: Proceedings of 13th Conference on USENIX Security Symposium (SSYM), vol 13, pp 21–37, San Diego, CA, USA (2004)
16 Moser, M., Bohme, R., Breuker, D.: An inquiry into money laundering tools in the Bitcoin ecosystem, pp 1–14 (2013) https://doi.org/10.1109/eCRS.2013.6805780
17 Erdin, E., Zachor, C., Gunes, M.: How to find hidden users: a survey of attacks on anonymity networks IEEE Commun Surv Tutor 17, 1–1 (2015) https://doi.org/10.1109/COMST.2015.2453434
A taxi driver registered with the Bitaksi application allegedly plotted to murder a passenger following a justified negative review This incident highlights the potential dangers of the gig economy and the extreme reactions that can arise from customer feedback Such cases underscore the importance of ensuring safety and accountability in ride-sharing services For more details, visit the Reddit post discussing this alarming situation.
19 Reid, F., Harrigan, M.: An analysis of anonymity in the bitcoin system Secur Privacy Soc. Netw 3 (2011) https://doi.org/10.1109/PASSAT/SocialCom.2011.79
20 Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., Mccoy, D., Voelker, G., Savage, S.:
A fistful of bitcoins: characterizing payments among men with no names, pp 127–140 (2013). https://doi.org/10.1145/2504730.2504747
21 Ortega, M.S.: The Bitcoin transaction graph anonymity M.S thesis, Security of Information and Communication Technologies, Universitat Autònoma de Barcelona, Barcelona, Spain,
22 Fleder, M., Kester, M., Pillai, S.: Bitcoin Transaction Graph Analysis (2015)
23 Spagnuolo, M., Maggi, F., Zanero, S.: BitIodine: Extracting Intelligence from the Bitcoin Network, vol 8437, pp 457–468 (2014) https://doi.org/10.1007/978-3-662-45472-5_29
24 Lischke, M., Fabian, B.: Analyzing the Bitcoin Network: The First Four Years Future Internet, vol 8 (2016) https://doi.org/10.3390/fi8010007
25 Alessandretti, L., Elbahrawy, A., Luca, M., Baronchelli, A.: Anticipating cryptocurrency prices using machine learning Complexity 2018, 1–16 (2018) https://doi.org/10.1155/2018/8983590
26 Corbet, S., Lucey, B., Urquhart, A., Yarovaya, L.: Cryptocurrencies as a financial asset: a systematic analysis Int Rev Fin Anal 62 (2018) https://doi.org/10.1016/j.irfa.2018.09.003
27 McNally, S., Roche, J., Caton, S.: Predicting the Price of Bitcoin Using Machine Learning, pp 339–343 (2018) https://doi.org/10.1109/PDP2018.2018.00060
28 Saad, M., Choi, J., Nyang, D., Kim, J., Mohaisen, A.: Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions IEEE Syst J., 1–12 (2019) https://doi.org/ 10.1109/JSYST.2019.2927707
29 Jang, H., Lee, J.: An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information IEEE Access, 1–1 (2017) https://doi.org/ 10.1109/ACCESS.2017.2779181
30 Nakano, M., Takahashi, A., Takahashi, S.: Bitcoin technical trading with artificial neural network Phys A Stat Mech Its Appl 510, 587–609 (2018) https://doi.org/10.1016/j.physa. 2018.07.017
31 Rebane, J., Karlsson, I., Denic, S., Papapetrou, P.: Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction: A Comparative Study (2018)
32 Huisu, J., Lee, J., Ko, H., Lee, W.: Predicting bitcoin prices by using rolling window LSTM model In: Proceedings of the KDD Data Science in Fintech Workshop, London, UK (2018)
33 Shintate, T., Pichl, L.: Trend prediction classification for high frequency bitcoin time series with deep learning J Risk and Fin Manage 12, 17 (2019) https://doi.org/10.3390/jrfm12 010017
34 Chen, G., Nikolov, S., Shah, D.: A latent source model for nonparametric time series classification Advances in Neural Information Processing Systems (2013)
35 Shah, D., Zhang, K.: Bayesian regression and Bitcoin In: 2014 52nd Annual Allerton Confer- ence on Communication, Control, and Computing, Allerton (2014) https://doi.org/10.1109/ ALLERTON.2014.7028484
36 Giaglis, G., Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D.: Using time-series and sentiment analysis to detect the determinants of bitcoin prices SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2607167(2015).
37 Matta, M., Lunesu, Maria I., Marchesi, M.: Bitcoin Spread Prediction Using Social and Web Search Media (2015)
38 Matta, M., Lunesu, M.I., Marchesi, M.: The Predictor Impact of Web Search Media on Bitcoin Trading Volumes.https://doi.org/10.5220/0005618606200626
39 Kristoufek, L.: What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis PLoS ONE (2014) https://doi.org/10.1371/journal.pone.0123923
40 Alex, G., Au, B.: Using the bitcoin transaction graph to predict the price of bitcoin (2015)
41 Madan, I., Saluja, S., Zhao, A.: Automated bitcoin trading via machine learning algorithms (2015)
42 Pham, T., Lee, S.: Anomaly Detection in the Bitcoin System—A Network Perspective (2016)
43 Xu, J.: Are blockchains immune to all malicious attacks? Financial Innovation 2 (2016) https:// doi.org/10.1186/s40854-016-0046-5
44 Smith, R., Bivens, A., Embrechts, M., Palagiri, C., Szymanski, B.: Clustering approaches for anomaly-based intrusion detection In: Proceedings of Intelligent Engineering Systems Through Artificial Neural Networks, pp 579–584 (2002)
45 Pham, T., Lee, S.: Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods (2016)
46 Monamo, P., Marivate, V., Twala, B.: Unsupervised learning for robust Bitcoin fraud detection, pp 129–134 (2016) https://doi.org/10.1109/ISSA.2016.7802939
47 Monamo, P., Marivate, V., Twala, B.: A Multifaceted Approach to Bitcoin Fraud Detection: Global and Local Outliers, pp 188–194 (2016) https://doi.org/10.1109/ICMLA.2016.0039
48 Zambre, D., Shah, A.: Analysis of bitcoin network dataset for fraud Unpublished Report (2013)
49 Bartoletti, M., Pes, B., Serusi, S.: Data Mining for Detecting Bitcoin Ponzi Schemes, pp 75–84
50 Zhdanova, M., Repp, J., Rieke, R., Gaber, C., Hemery, B.: No Smurfs: Revealing Fraud Chains in Mobile Money Transfers (2014) https://doi.org/10.1109/ARES.2014.10
51 Harlev, M., Yin, H., Langenheldt, K., Mukkamala, R R., Vatrapu, R.: Breaking Bad: De- Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning
52 Yin, H., Vatrapu, R.: A first estimation of the proportion of cybercriminal entities in the bitcoin ecosystem using supervised machine learning, pp 3690–3699 (2017) https://doi.org/10.1109/ BigData.2017.8258365
53 Hirshman, J., Huang, Y., Macke, S.: Unsupervised approaches to detecting anomalous behavior in the bitcoin transaction network, Technical report, Technical report, Stanford University (2013)
54 Liu, Z., Zhao, H., Chen, W., Cao, X., Peng, H., Yang, J., Yang, T., Lin, P.: Double-Spending Detection for Fast Bitcoin Payment Based on Artificial Immune, pp 133–143 (2017) https:// doi.org/10.1007/978-981-10-6893-5_10
In the paper "Seeing is Understanding: Anomaly Detection in Blockchains with Visualized Features," authored by A Bogner and presented at the International Joint Conference on Pervasive and Ubiquitous Computing and the International Symposium on Wearable Computers, the author explores the significance of visualizing features to enhance the detection of anomalies within blockchain technology The research, published by ACM in 2017, highlights the importance of visualization techniques in improving understanding and analysis of blockchain data, ultimately contributing to more effective anomaly detection strategies.
56 Cazabet, R., Rym, B., Latapy, M.: Tracking Bitcoin Users Activity Using Community Detection on a Network of Weak Signals, pp 166–177 (2018) https://doi.org/10.1007/978-3-319-72150- 7_14
57 Kurtulmus, A., Daniel, K.: Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain (2018)
58 Shaukat, S., Ribeiro, V.: RansomWall: A layered defense system against cryptographic ransomware attacks using machine learning, pp 356–363 (2018) https://doi.org/10.1109/COM SNETS.2018.8328219
59 Baqer, K., Huang, D., Mccoy, D., Weaver, N.: Stressing Out: Bitcoin “Stress Testing”, vol.
60 Holub, A., O’Connor, J.: Coinhoarder: Tracking a Ukrainian Bitcoin phishing ring DNS style. In: APWG Symposium on Electronic Crime Research (eCrime), 2018, pp 1–5 IEEE (2018)
61 Ermilov, D., Panov, M., Yanovich, Y.: Automatic Bitcoin Address Clustering, pp 461–466
62 Dey, S.: Securing Majority-Attack in Blockchain Using Machine Learning and Algorithmic Game Theory: A Proof of Work, pp 7–10 (2018) https://doi.org/10.1109/CEEC.2018.867 4185
63 Portnoff, R., Huang, D., Doerfler, P., Afroz, S., Mccoy, D: Backpage and Bitcoin: UncoveringHuman Traffickers, pp 1595–1604 (2017) https://doi.org/10.1145/3097983.3098082
64 Biryukov, A., Khovratovich, D., Pustogarov, I.: Deanonymisation of clients in bitcoin P2P network In: Proceedings of the ACM Conference on Computer and Communications Security
65 Box, G., E., P., Jenkins, G., Reinsel, G., Ljung, G.: Time Series Analysis: Forecasting andControl (2016) https://doi.org/10.2307/2284112
Evolutionary Transformation of Blockchain Technology
Pratyusa Mukherjee and Chittaranjan Pradhan
Blockchain technology has rapidly gained popularity due to its ability to revolutionize peer-to-peer information exchange through decentralization, immutability, and transparency Defined as a distributed ledger, blockchain secures each record or block using cryptographic hash functions, creating a continuous chain of blocks This chapter explores the historical background of blockchain, introduces essential terminologies, outlines its various types, and describes the basic structure of a block along with widely recognized consensus models The primary focus is to provide a comprehensive study of the chronological evolution of blockchain technology, detailing the significant developments in each generation.
This article highlights the differences across various generations of blockchain technology, focusing on key parameters such as primary applications, consensus models, smart contract utility, energy and cost efficiency, execution speed, and scalability Additionally, it presents a detailed case study on the implementation of blockchain in supply chain management.
Keywords BlockchainãDistributed ledgerã Bitcoin ã Ethereum ãHyperledger fabricãCryptographic hashãConsensus models
Introduction
The CIA triad—confidentiality, integrity, and availability—is essential for any cryptosystem aiming to secure critical and sensitive data Data theft remains a significant cybersecurity challenge that undermines these core objectives To address this issue, various technologies have been developed, with Blockchain Technology emerging as a recent solution Both data in transit and data at rest are susceptible to theft and unauthorized access, complicating the process of tracking down cybercriminals.
School of Computer Engineering, KIIT Deemed to be University, Bhubaneshwar, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
S K Panda et al (eds.), Blockchain Technology: Applications and Challenges,
Intelligent Systems Reference Library 203, https://doi.org/10.1007/978-3-030-69395-4_3
Blockchain technology significantly mitigates the challenges associated with traditional data management by providing a distributed database that records every transaction among relevant parties Each transaction undergoes rigorous verification, ensuring that once information is entered into the blockchain, it remains immutable and can only be altered with the consent of all involved parties.
Blockchains function as public registers that record transactions in a series of interconnected blocks, relying on cryptography and distributed systems Each block contains the hash of the previous block, ensuring that any changes to prior data result in a hash mismatch, making it easy to identify tampering This tamperproof characteristic eliminates the need for a central authority for validation Additionally, the distributed nature of blockchains, with multiple copies stored across various networks, complicates unauthorized modifications and significantly enhances their security.
Blockchain technology gained prominence with the launch of Bitcoin by Satoshi Nakamoto in 2008, evolving significantly since its inception The first generation, represented by Bitcoin, introduced a decentralized peer-to-peer digital currency that removed the need for central authorities like banks To build trust among users, Bitcoin employs consensus models that ensure the authenticity and integrity of transactions However, due to its limited functionality primarily in the financial sector, advancements led to the emergence of second-generation blockchain technology, such as Ethereum, which expands its applications to areas like crowdsourcing through reliable smart contracts.
The emergence of autogenously assertive contracts allows for direct compliance between buyers and sellers to be encoded within a decentralized blockchain network Hyperledger enhances this landscape by offering greater modularity and versatility compared to Ethereum, thanks to its permissioned architecture Additionally, third-generation blockchain technology incorporates built-in verification mechanisms, making transactions faster, more efficient, and cost-effective The integration of Artificial Intelligence with blockchain technology is already setting the stage for the fourth generation of blockchain advancements.
This chapter first gives the historical background of this expeditious technology.
This chapter provides a comprehensive overview of blockchain technology, including essential terminologies, various types, and the fundamental structure of blocks, along with popular consensus models It emphasizes the chronological evolution of blockchain technology, detailing the unique characteristics of each generation Key differences are highlighted across several parameters, such as principle areas, consensus models, smart contract utility, energy and cost requirements, as well as execution speed and scalability Additionally, a case study on the application of blockchain in Supply Chain Management is presented to illustrate its practical implications.