Blockchains provide decentralised, tamper-free registries of transactions among partners that may not trust each other. For the scientific community, blockchain smart contracts have been proposed to decentralise and make more transparent multiple aspects of scholarly communications. We show how an Ethereum-based suite of smart contracts running on top of a Web-enabled governance framework can facilitate decentralised computation of citations that is trustworthy. We implement and evaluate Smart Papers, and extend it with a model for decentralised citation counts. We show how our approach complements current models for decentralised publishing and informetrics calculation, and analyse cost and performance implications.
Samvid Dharanikota, Michael Jensen, Sebastian Rom Kristensen, Mathias Sass Michno · 7 authors
Randomness beacons are services that periodically emit a random number, allowing users to base decisions on the same random value without trusting anyone: ideally, the randomness beacon does not only produce unpredictable values, but is also of low computational complexity for the users, bias-resistant and publicly verifiable. Such randomness beacons can serve as an important primitive for smart contracts in a variety of contexts. This paper first presents a structured security analysis, based on which we then design, implement, and evaluate a trustworthy and efficient randomness beacon. Our approach does not require users to register or run any computationally intensive operations. We then compare different implementation and deployment options on distributed ledgers, and report on an Ethereum smart contract-based lottery using our beacon.
Each of the most popular blockchain platform, Bitcoin, Ethereum, and Hyperledger Fabric, varies in aspects of decentralization, permission, anonymity, and native-currency, has its own consensus mechanism, algorithm and implementation. In the mainstream blockchain technology, there are many common consensus algorithms. They differ in terms of computational complexity, fault-tolerance, and resilience. The performance, consistency, scalability, and efficiency of blockchain consensus mechanism need further improvement and optimization. Consensus mechanism and code implementation of Bitcoin, Ethereum and Hyperledger are analyzed, discussed and proposed.
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).
Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.
Patrick L. Chapman, Dianxiang Xu, Lin Deng, Yin Xiong
Blockchain in recent years has exploded in popularity with Ethereum being one of the leading blockchain platforms. Solidity is a widely used scripting language for creating smart contracts in Ethereum applications. Quality assurance in Solidity contracts is of critical importance because bugs or vulnerabilities can lead to a considerable loss of financial assets. However, it is unclear what level of quality assurance is provided in many of these applications. Mutation testing is the process of intentionally injecting faults into a target program and then running the provided test suite against the various injected faults. Mutation testing is used to evaluate the effectiveness of a test suite, measuring the test suiteâs capability of covering certain types of faults. This thesis presents Deviant, the first implementation of a mutation testing tool for Solidity smart contracts. Deviant implements mutation operators that cover the unique features of Solidity according to our constructed fault model, in addition to traditional mutation operators that exist for other programming languages. Deviant has been applied to five open-source Solidity projects: MetaCoin [30], MultiSigWallet [31], Alice [29], aragonOS [32], and OpenZeppelin [33]. Experimental results show that the provided test suites result in low mutation scores. These results indicate that the provided tests cannot ensure high-level assurance of code quality. Such evaluation results offer important guidelines for Solidity developers to implement more effective tests in order to deliver trustworthy code and reduce the risk of financial loss.
Ethereum is the largest blockchain platform supporting smart contracts with the second biggest market capitalization. Ethereum data can yield many useful insights because of the large volume of transactions, accounts and blocks as well as the popular applications developed as smart contracts. Studying Ethereum data can also reveal many new attacks to the platform and its smart contracts. Unfortunately, it is non-trivial to systematically explore Ethereum because it involves massive heterogeneous data, which are produced and stored in different ways. Although a few recent studies report some interesting observations about Ethereum, they are limited by their data acquisition methods which cannot provide comprehensive and precise data. In this paper, to fill the gap, we propose DataEther, a systematic and high-fidelity data exploration framework for Ethereum by exploiting its internal mechanisms. Besides supporting the analyses in existing studies, DataEther further empowers users to explore unknown phenomena and obtain in-depth understandings. We first describe how we tackle the challenging issues in developing DataEther, and then use four data-centric applications to demonstrate its usage and report many new observations.
Background/Objectives: Ethereum is a next-generation blockchain developed in 2014 by Vitalik Buterin, a cryptocurrency that can implement various smart contracts. Methods/Statistical analysis: When making any contract on a blockchain, the miner is given the corresponding compensation, the block, which is obtained earlier than the hash of the set size by applying a random nonce value and hash algorithm according to the prop-off-work method. In the case of Ethereum, the ETH will be given to the miner who uses more gas in addition to the work proof method. Findings: In this case, if the malicious miner increases the amount of gas first, obtains the priority for block generation, intercepts the value derived by the general miner and sends it to the miner, the miner will give the block to the malicious miner. As such, miner, a problem that arises due to the nature of blockchain, analyzes the problem of relying on transaction order that gives the miner a block option to the miner who uses more gas and proposes a solution. Improvements/Applications: we described how to solve the problem of pre-sales in the blockchain with public key cryptography using confidentiality. It is hoped that this approach will be fairer and safer.
Summary Contemporary blockchain such as Bitcoin and Ethereum execute transactions serially by miners and validators and determine the ProofâofâWork (PoW). Such serial execution is unable to exploit modern multiâcore resources efficiently, hence limiting the system throughput and increasing the transaction acceptance latency. The objective of this work is to increase the transaction throughput by introducing parallel transaction execution using a static analysis over the transaction dependencies. We propose the DiPETrans framework for distributed execution of transactions in a block. Here, peers in the blockchain network form a community of trusted nodes to execute the transactions and find the PoW inâparallel, using a leaderâfollower approach. During mining, the leader statically analyzes the transactions, creates different groups (shards) of independent transactions, and distributes them to followers to execute concurrently. After execution, the community's compute power is utilized to solve the PoW concurrently. When a block is successfully created, the leader broadcasts the proposed block to other peers in the network for validation. On receiving a block, the validators reâexecute the block transactions and accept the block if they reach the same state as shared by the miner. Validation can also be done in parallel, following the same leaderâfollower approach as mining. We report experiments using over 5 million real transactions from the Ethereum blockchain and execute them using our DiPETrans framework to empirically validate the benefits of our techniques over a traditional sequential execution. We achieve a maximum speedup of 2.2 and 2.0 and an average speedup of 1.6 and 1.5 for the miner and the validator, respectively, with 100â500 transactions per block when using 6 machines in the community. Further, we achieve a peak of 5 endâtoâend block creation speedup using a parallel miner over a serial miner.
Ride-sharing is a service that enables drivers to share trips with other riders, contributing to appealing benefits of shared travel cost and reducing traffic congestion. However, the majority of existing ride-sharing services rely on a central third party to organize the service, which make them subject to a single point of failure and privacy disclosure concerns by both internal and external attackers. Moreover, they are vulnerable to distributed denial of service (DDoS) and Sybil attacks launched by malicious users and external attackers. Besides, high service fees are paid to the ride-sharing service provider. In this paper, we propose a decentralized ride-sharing service based on public Blockchain, named B-Ride. B-Ride enables drivers to offer ride-sharing services without relying on a trusted third party. Both riders and drivers can learn whether they can share rides while preserving their trip data, including pick-up/drop-off location, departure/arrival date and travel price. However, malicious users exploit the anonymity provided by the public blockchain to submit multiple ride requests or offers, while not committing to any of them, in order to find a better offer or to make the system unreliable. B-Ride solves this problem by introducing a time-locked deposit protocol for a ride-sharing by leveraging smart contract and zero-knowledge set membership proof. In a nutshell, both a driver and a rider have to show their good will and commitment by sending a deposit to the blockchain. Later, a driver has to prove to the blockchain on the agreed pick-up time that he/she arrived at the pick-up location on time. To preserve rider/driver privacy by hiding the exact pick-up location, the proof is performed using zero-knowledge set membership proof. Moreover, to ensure fair payment, a pay-as-you-drive methodology is introduced based on the elapsed distance of the driver and rider. In addition, we introduce a reputation model to rate drivers based on their past behaviour without involving any third-parties to allow riders to select them based on their history on the system. Finally, we implement our protocol and deploy it in a test net of Ethereum. The experimental results show the applicability of our protocol atop existing real-world blockchains.
Ride-sharing is a service that enables drivers to share their trips with\nother riders, contributing to appealing benefits of shared travel costs.\nHowever, the majority of existing platforms rely on a central third party,\nwhich make them subject to a single point of failure and privacy disclosure\nissues. Moreover, they are vulnerable to DDoS and Sybil attacks due to\nmalicious users involvement. Besides, high fees should be paid to the service\nprovider. In this paper, we propose a decentralized ride-sharing service based\non public Blockchain, named B-Ride. Both riders and drivers can find rides\nmatch while preserving their trip data, including pick-up/drop-off location,\nand departure/arrival date. However, under the anonymity of the public\nblockchain, a malicious user may submit multiple ride requests or offers, while\nnot committing to any of them, to discover better offer or to make the system\nunreliable. B-Ride solves this problem by introducing a time-locked deposit\nprotocol for a ride-sharing by leveraging smart contract and zero-knowledge set\nmembership proof. In a nutshell, both a driver and a rider have to show their\ncommitment by sending a deposit to the blockchain. Later, a driver has to prove\nto the blockchain on the agreed departure time that he has arrived at the\npick-up location. To preserve rider/driver location privacy by hiding the exact\npick-up location, the proof is done using zero-knowledge set membership\nprotocol. Moreover, to ensure a fair payment, a pay-as-you-drive methodology is\nintroduced based on the elapsed distance of the driver and the rider. Also, we\nintroduce a reputation-based trust model to rate drivers based on their past\ntrips to allow riders to select them based on their history on the system.\nFinally, we implement B-Ride in a test net of Ethereum. The experiment results\nshow the applicability of our protocol atop the existing real-world blockchain.\n
BACKGROUND: In recent years, researchers have made significant efforts in advancing blockchain technology. This technology, with distinct features of decentralization and security, can be applied to many fields. In areas of health data and resource sharing, applications of blockchain technology are also emerging. OBJECTIVE: In this study, we propose a cloud health resource-sharing model based on consensus-oriented blockchain technology and have developed a simulation study on breast tumor diagnosis. METHODS: The proposed platform is built on a consortium or federated blockchain that possesses features of both centralization and decentralization. The consensus mechanisms generate operating standards for the proposed model. Open source Ethereum code is employed to provide the blockchain environment. Proof of Authority is selected as the consensus algorithm of block generation. RESULTS: Based on the proposed model, a simulation case study for breast tumor classification is constructed. The simulation includes 9893 service requests from 100 users; 22 service providers are equipped with 22 different classification methods. Each request is fulfilled by a service provider recommended by the weighted k-nearest neighbors (KNN) algorithm. The majority of service requests are handled by 9 providers, and provider service evaluation scores tend to stabilize. Also, user priority on KNN weights significantly affects the system operation outcome. CONCLUSIONS: The proposed model is feasible based on the simulation case study for the cloud service of breast tumor diagnosis and has the potential to be applied to other applications.
Based on the high-frequency recordings from Kraken, a cryptocurrency exchange and professional trading platform that aims to bring Bitcoin and other cryptocurrencies into the mainstream, the multiscale cross-correlations involving the Bitcoin (BTC), Ethereum (ETH), Euro (EUR) and US dollar (USD) are studied over the period between 1 July 2016 and 31 December 2018. It is shown that the multiscaling characteristics of the exchange rate fluctuations related to the cryptocurrency market approach those of the Forex. This, in particular, applies to the BTC/ETH exchange rate, whose Hurst exponent by the end of 2018 started approaching the value of 0.5, which is characteristic of the mature world markets. Furthermore, the BTC/ETH direct exchange rate has already developed multifractality, which manifests itself via broad singularity spectra. A particularly significant result is that the measures applied for detecting cross-correlations between the dynamics of the BTC/ETH and EUR/USD exchange rates do not show any noticeable relationships. This could be taken as an indication that the cryptocurrency market has begun decoupling itself from the Forex.
Min Choi, Rupali Shinde, Sechang Oh, OhâYoung Kwon
In this paper, we present a badge awarding system for performance assessment in education using blockchain technology. Learners will be awarded badges, which are certified for a predetermined level of progress in terms of learning. All the badges are stored in a backpack, which is an environment for storing and presenting the obtained badges. Badges are immutable and verifiable as well as rigid. The use of badges in the education system makes education more interesting, skill-based, and adaptable to changes. The key contribution of our work is in terms of compatibility with Openbadge specification and integrated cooperating platform between digital badge awarding and blockchaining. Our system implementation is compatible with Open Badges of IMS Global Learning Consortium, which is used to earn, issue, and award badges across various platforms. The badges are trusted by the IMS standard, the criteria to earn a badge is verified through the network, and the overall process is transparent compared to the traditional education system. Moreover, all badge awarding events in our system are recorded in a blockchain. Once stored, the contents cannot be tampered with. Thereafter, anyone can check the validity of the badge through the blockchain. Our platform will be useful for distance learning as well as time and location independent learning. The experimental results are as follows. On a Bitcoin-based digital badge publishing platform, the execution time required to award the badge is 24.53 s, while on the Ethereum-based digital badge publishing platform, the execution time to award the badge is only 3.86 s. It can also be used for career management and personal history. The learners can obtain the current knowledge required for a job. Lifelong learning will be also possible with this platform.
Proof-of-Storage (PoS) is a collective term for protocols that allow proving data integrity and availability. There exist several PoS schemes. While they differ in detailed specifications, their common primary advantage is eliminating the need for trust between storage providers and data owners. However, there does not exist a mechanism to provide self-emerging delivery of requests for proof of storage, commonly known as challenges.\n\nThis paper presents a decentralized system for PoS using self-emerging challenges built on smart contract in the Ethereum platform. Self-emerging challenges provide an automated mechanism for ensuring integrity and persistence of data at chosen time intervals. The design employs participating nodes in the Ethereum blockchain, commonly referred to as peers, to store and route challenges to storage providers. The peers are compensated for their service by their respective employers. Data owners are enabled to schedule the time of emergence of a challenge to storage providers. Upon a received challenge, storage providers prove the integrity and persistence of data by responding correctly to the challenge. The design builds on the existing work of decentralized self-emerging data systems over Ethereum blockchain networks. We show that this work can be utilized for PoS and solve the problems that the incorporation and adaptation of this work raises.\n\nWe evaluate the proposed system based on several factors. We investigate the security of the system based on the different attacks that the participants may execute for exploitation. Moreover, we evaluate the attractiveness of participating in the system based on the gained remuneration by peers and the positive reputation gained by storage providers for proving the integrity of their clientsâ data. We also evaluate the expenses of data owners utilizing the proposed system based on the inherited costs of invoking smart contract functions in the Ethereum platform. Lastly, through analysis, we find that to minimize the total costs in the system, the number of employed peers should be restricted to one in each path. In other words, one peer to deliver a PoS challenge to the storage service provider. We show that this additionally improves the fairness of remuneration payout to peers and analyze how security is affected by always utilizing one peer in each path. We discover that this improves prevention against drop attacks, while it to some degree decreases the prevention of release-ahead attacks which we deem less critical. Through these analyses, we recognize that the benefits greatly outweigh the drawback, and we make a suggestion that data owners should select exactly one peer per path in their services.
Fangyu Gai, Cesar Grajales, Jianyu Niu, Jalalzai, Mohammad Mussadiq · 5 authors
Sidechain technology has been envisioned as a promising solution to accelerate today's public blockchains in terms of scalability and interoperability. By relying on the mainchain for security, different sidechains can formulate their own rules to reach consensus. Although the literature has considered the possibility of using consensus protocols in the sidechain, so far a tailor-made consensus protocol for sidechains with high performance and formal security proof has not been attempted. To fill this gap, we introduce Cumulus, a low overhead, highly efficient, security provable sidechain protocol. Cumulus makes use of smart contracts to ensure that only one block proposed in the sidechain will be enforced on the mainchain in each round, thereby achieving consensus in an efficient manner. We give a formal specification of Cumulus which ensures safety and liveness without any online requirements of clients. For security analysis, we provide formal security definitions and proofs under Universally Composable Security (UCS) model. As a proof of concept, we implement Cumulus and evaluate it in an Ethereum testnet.
The emergence of e-commerce and e-voting platforms has resulted in the rise in the volume of sensitive information over the Internet. This has resulted in an increased demand for secure and private means of information computation. Towards this, the Yao's Millionaires' problem, i.e., to determine the richer among two millionaires' securely, finds an application. In this work, we present a new solution to the Yao's Millionaires' problem namely, Privacy Preserving Comparison (PPC). We show that PPC achieves this comparison in constant time as well as in one execution. PPC uses semi-honest third parties for the comparison who do not learn any information about the values. Further, we show that PPC is collusion-resistance. To demonstrate the significance of PPC, we present a secure, approximate single-minded combinatorial auction, which we call TPACAS, i.e., Truthful, Privacy-preserving Approximate Combinatorial Auction for Single-minded bidders. We show that TPACAS, unlike previous works, preserves the following privacies relevant to an auction: agent privacy, the identities of the losing bidders must not be revealed to any other agent except the auctioneer (AU), bid privacy, the bid values must be hidden from the other agents as well as the AU and bid-topology privacy, the items for which the agents are bidding must be hidden from the other agents as well as the AU. We demonstrate the practicality of TPACAS through simulations. Lastly, we also look at TPACAS' implementation over a publicly distributed ledger, such as the Ethereum blockchain.
Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.
Imtiaz Sifat, Azhar Mohamad, Mohammad Syazwan Bin Mohamed Shariff
This paper investigates lead-lag relationship between heavyweight cryptocurrencies Bitcoin and Ethereum. Traditional studies of information flow between markets preponderate on cash vs. futures, whereby researchers are interested in the stabilizing impact of futures on spot markets. While interest in the same relationship in the nascent cryptocurrency sphere is emerging, little is known regarding price leadership between these assets. In this paper, we employ a battery of statistical testsâVECM, Granger Causality , ARMA, ARDL and Wavelet Coherenceâto identify price leadership between the two crypto heavyweights Bitcoin and Ethereum. Based on one year hourly and daily data from August 2017 through to September 2018, our tests yield varied results but largely suggest bi-directional causality between the two assets. Moreover, the results indicate that intraday crypto traders can barely exploit Bitcoin-Ethereum hourly or daily price discovery process to their advantage.
Josep LluĂs Ferrer Gomila, M. Francisca Hinarejos, Andreu Pere Isern-DeyĂ
Electronically signing contracts is fundamental for e-commerce transactions. The main property that contract signing protocols must achieve is fairness of the exchange. The solutions presented to date are divided into two major types: those that have a trusted third party (TTP) to achieve fairness and those that do not. In the literature, we find more than 40 published proposals, but none of these proposed protocols has become a recognized or de facto standard in the market. Blockchain has provided a new way to address classic problems such as double spending, as well as problems such as fairness. In this article, we present a protocol for contract signing based on blockchain. Our proposed protocol does not require a conventional TTP, and it does not present the disadvantages of solutions without a TTP (computational and/or communication cost). The protocol satisfies the necessary security requirements: fairness, timeliness and non-repudiation. We demonstrate the feasibility of the protocol with a cost analysis and a proof of concept implementation. In addition, we show how Ethereum can be integrated in our solution as an alternative platform to the use of Bitcoin. Finally, we show how our proposal improves previous solutions for contract signing based on blockchain in terms of cost, efficiency and security.
Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Gewekeâs autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiberâs general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z -score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.
Ethereum smart contracts are public, immutable and distributed and, as such, they are prone to vulnerabilities sourcing from programming mistakes of developers. This paper presents SAFEVM, a verification tool for Ethereum smart contracts that makes use of state-of-the-art verification engines for C programs. SAFEVM takes as input an Ethereum smart contract (provided either in Solidity source code, or in compiled EVM bytecode), optionally with assert and require verification annotations, and produces in the output a report with the verification results. Besides general safety annotations, SAFEVM handles the verification of array accesses: it automatically generates SV-COMP verification assertions such that C verification engines can prove safety of array accesses. Our experimental evaluation has been undertaken on all contracts pulled from etherscan.io (more than 24,000) by using as back-end verifiers CPAchecker, SeaHorn and VeryMax.
A Coordination Blockchain is a blockchain with the task of coordinating activities of multiple private blockchains. This paper discusses the pros and cons of using Ethereum MainNet, the public Ethereum blockchain, as a Coordination Blockchain. The requirements Ethereum MainNet needs to fulfil to perform this role are discussed within the context of Ethereum Private Sidechains, a private blockchain technology which allows many blockchains to be operated in parallel, and allows atomic crosschain transactions to execute across blockchains. Ethereum MainNet is a permissionless network which aims to offer strong authenticity, integrity, and non-repudiation properties, that incentivises good behaviour using crypto economics. This paper demonstrates that Ethereum MainNet does deliver these properties. It then provides a comprehensive review of the features of Ethereum Private Sidechains, with a focus on the potential usage of Coordination Blockchains for these features. Finally, the merits of using Ethereum MainNet as a Coordination Blockchain are assessed. For Ethereum Private Sidechains, we found that Ethereum MainNet is best suited to storing long term static data that needs to be widely available, such as the Ethereum Registration Authority information. However, due to Ethereum MainNet's probabilistic finality, it is not well suited to information that needs to be available and acted upon immediately, such as the Sidechain Public Keys and Atomic Crosschain Transaction state information that need to be accessible prior to the first atomic crosschain transaction being issued on a sidechain. Although this paper examined the use of Ethereum MainNet as a Coordination Blockchain within reference to Ethereum Private Sidechains, the discussions and observations of the typical tasks a Coordination blockchain may be expected to perform are applicable more widely to any multi-blockchain system.
A Coordination Blockchain is a blockchain with the task of coordinating\nactivities of multiple private blockchains. This paper discusses the pros and\ncons of using Ethereum MainNet, the public Ethereum blockchain, as a\nCoordination Blockchain. The requirements Ethereum MainNet needs to fulfil to\nperform this role are discussed within the context of Ethereum Private\nSidechains, a private blockchain technology which allows many blockchains to be\noperated in parallel, and allows atomic crosschain transactions to execute\nacross blockchains. Ethereum MainNet is a permissionless network which aims to\noffer strong authenticity, integrity, and non-repudiation properties, that\nincentivises good behaviour using crypto economics. This paper demonstrates\nthat Ethereum MainNet does deliver these properties. It then provides a\ncomprehensive review of the features of Ethereum Private Sidechains, with a\nfocus on the potential usage of Coordination Blockchains for these features.\nFinally, the merits of using Ethereum MainNet as a Coordination Blockchain are\nassessed. For Ethereum Private Sidechains, we found that Ethereum MainNet is\nbest suited to storing long term static data that needs to be widely available,\nsuch as the Ethereum Registration Authority information. However, due to\nEthereum MainNet's probabilistic finality, it is not well suited to information\nthat needs to be available and acted upon immediately, such as the Sidechain\nPublic Keys and Atomic Crosschain Transaction state information that need to be\naccessible prior to the first atomic crosschain transaction being issued on a\nsidechain. Although this paper examined the use of Ethereum MainNet as a\nCoordination Blockchain within reference to Ethereum Private Sidechains, the\ndiscussions and observations of the typical tasks a Coordination blockchain may\nbe expected to perform are applicable more widely to any multi-blockchain\nsystem.\n