Carlos Henrique Fernandes de Faria, Miguel Correia
A blockchain is a distributed ledger in which participants that do not fully trust each other agree on the ledger's content by running a consensus algorithm. This technology is raising a lot of interest both in academia and industry, but the lack of tools to evaluate design and implementation decisions may hamper fast progress. To address this issue, this paper presents a discrete-event simulator that is flexible enough to evaluate different blockchain implementations. These blockchains can be rapidly modeled and simulated by extending existing models. Running Bitcoin and Ethereum simulations allowed us to change conditions and answer different questions about their performance. For example, we concluded that doubling the number of transactions per block has a low impact on the block propagation delay (10ms) and that encrypting communication has a high impact in that delay (more than 25%).
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 suite’s 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.
Gbadebo Ayoade, Erick Bauman, Latifur Khan, Kevin W. Hamlen
An Ethereum bytecode rewriting and validation architecture is proposed and evaluated for securing smart contracts in decentralized cryptocurrency systems without access to contract source code. This addresses a wave of smart contract vulnerabilities that have been exploited by cybercriminals in recent years to steal millions of dollars from victims. Such attacks have motivated various best practices proposals for helping developers write safer contracts; but as the number of programming languages used to develop smart contracts increases, implementing these best practices can be cumbersome and hard to enforce across the development tool chain. Automated hardening at the bytecode level bypasses this source-level heterogeneity to enforce safety and code integrity properties of contracts independently of the sources whence they were derived. In addition, a binary code verification tool implemented atop the Coq interactive theorem prover establishes input-output equivalence between the original code and the modified code. Evaluation demonstrates that the system can enforce policies that protect against integer overflow and underflow vulnerabilities in real Ethereum contract bytecode, and overhead is measured in terms of instruction counts.
Amjad Aldweesh, Maher Alharby, Maryam Mehrnezhad, Aad van Moorsel
Ethereum and other blockchains rely on miners contributing computational power to execute tasks such as the proof of work consensus mechanism and the execution and validation of smart contracts. Miners receive a fee, and for the correct operation of the blockchain, rewards should be proportional to the required investment (equipment, energy use, etc.). In Ethereum, the reward obtained for executing smart contracts is set statically, associating a fee with each operation code (opcode) in the smart contract. To determine whether fees are aligned with investments made, we propose OpBench, a platform-independent benchmarking approach. OpBench measures the CPU time required to execute opcodes in the Ethereum Virtual Machine. We implemented OpBench for the PyEthApp and Go-Ethereum clients, and present results for both platforms on three different machines and operating systems. The results show that the static fees set by Ethereum are not always proportional to the invested CPU time, with up to an order of magnitude difference across opcodes. The results also show a markable difference in performance between clients, with the Go client outperforming the Python client across machines.
Decentralized attestation methods for blockchains are currently being discussed and standardized for use cases such as certification, identity and existence proofs. In a blockchain-based attestation, a claim made about the existence of information can be cryptographically verified publicly and transparently. In this paper we explore the attestation of models through globally unique identifiers as a first step towards decentralized applications based on models. As a proof-of-concept we describe a prototypical implementation of a software connector for the ADOxx metamodeling platform. The connector allows for (a.) the creation of claims bound to the identity of an Ethereum account and (b.) their verification on the blockchain by anyone at a later point in time. For evaluating the practical applicability, we demonstrate the application on the Ethereum network and measure and evaluate limiting factors related to transaction cost and confirmation times.
Blockchain technologies have recently received considerable attention, partly due to the success of cryptocurrency applications such as Bitcoin and Ethereum. As the adoption of blockchain technologies by various sectors increases, there is a demand for tools that enable regulatory enforcement, which include monitoring, examining and ensuring compliance of the data stored by the blockchain systems, all in a privacy preserving way. Current blockchain solutions store transactions in append-only and immutable fashion without any indexing, which contributes to limited and inefficient queries. Additionally, there is no support for privacy-preserving query processing. To address these issues, in this paper, we propose a system that can provide auditors, enforcing regulatory compliance, with efficient, scalable and richer blockchain query processing over Hadoop and synchronized Ethereum clients. The system additionally ensures auditors' privacy by utilizing cryptography techniques over semi-trusted servers to protect the auditors' identities, queries and their results.
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.
Eunjin Jung, Marion Le Tilly, Ashish Gehani, Yunjie Ge
The popularity of blockchain-based currencies, such as Bitcoin and Ethereum, has grown among enthusiasts since 2009. Relying on the anonymity provided by the blockchain, hustlers have adapted offline scams to this new ecosystem. As a result, Ponzi schemes are proliferating on Ethereum, dressed up as secure investment schemes. They reward early investors with funds from the later ones before collapsing, leaving the last investors empty handed. Illegal in the offline world, they are creating thousands of victims on Ethereum, while stealing millions of dollars worth of ether. We use data mining to provide a detection model for Ponzi schemes on Ethereum, improving over prior work. We built a dataset of likely benign Ethereum smart contracts, in addition to known Ponzi scheme smart contracts, and designed features based on their compiled code and transactions. Using Weka to benchmark several classification algorithms, we obtained models that achieve both high precision and high recall. Our 0-day model can be used as soon as a smart contract is uploaded on the blockchain. The full-feature model continued to show high performance for almost 250 days. A detailed analysis on top-strength features provides novel perspectives on Ponzi scheme behavior.
Supply chain in industry is a flow of information and goods traversal through multiple suppliers such as raw material suppliers, manufacturers, distributors, retailers and end users. Supply chain management (SCM) helps in smooth monitoring and accounting of information and goods traversal among multiple users. However, the existing supply chain management processes lack in offering transparency of information, user's privacy, timely updates on demand peaks, improper tracing of information, quality management, deal repudiation and trust among users. To address these problems, we propose a novel framework for pharmaceutical supply chain management which uses an ethereum blockchain, a distributed ledger technology. We propose different smart contracts to manage pharmaceutical supply chain processes over an ethereum blockchain. This framework helps in achieving user privacy, transparency of the supplied drug, tracking of drugs, quality management, non-repudiation and demand supply management with the improved trust among the users. At the end, we analyze the proposed framework in terms of fulfilling the pharmaceutical supply chain management requirements.
Blockchain Technology Applications and Security
Innovative Microfluidic and Catalytic Techniques Innovation
Zhongli Dong, Emma Zheng, Young Choon, Albert Y. Zomaya
Directed Acyclic Graph (DAG) has been emerging as the so-called Blockchain 3.0 after Bitcoin (Blockchain 1.0) and Ethereum (Blockchain 2.0). This new distributed ledger technology is getting significant attention for its high performance and low transaction fee. There have already been several notable implementations, such as IOTA [1], Nano [2] and Byteball [3]. In this paper, we present DAGBENCH as a performance evaluation framework for DAG implementations. DAGBENCH provides a number of sample workloads and adaptors that make effective and easy evaluation of different DAG implementations. It allows any DAG implementation to be evaluated by adding an adaptor. DAGBENCH allows to measure the performance of DAG implementation in terms of throughput, latency, scalability, success indicator, resource consumption, transaction data size and transaction fee. We demonstrate the efficacy of DAGBENCH with different DAG implementations. In particular, we have conducted experiments, on Amazon EC2, with three popular DAG implementations: IOTA, Nano and Byteball. Our experimental results provide the performance comparison between these implementations that helps developers/users effectively evaluate different performance characteristics; and, this enables them to identify bottlenecks and accordingly to improve performance.
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.
Asset pricing models and investment styles have been researched intensively in equities, bonds, FX, and commodities. However, a new asset class has emerged since the end of 2008, namely, cryptocurrencies such as Bitcoin and Ethereum, among others. The author uses an extensive data set of over 1,500 cryptocurrencies and shows that almost none of the traditional investment styles such as momentum or defensive appear to be successful in this young asset class. Cryptocurrencies are also independent from the macroeconomic environment and cannot be explained by a standard asset pricing model. A cryptocurrency specific model yields clearly better results. In addition, the whole cryptocurrency space is dominated by only a few individual digital coins. Equally weighted mean monthly returns appear to be random with low or even no correlation with traditional asset classes such as US equities and global FX. <b>TOPICS:</b>Currency, portfolio construction, risk management, performance measurement
Purpose The purpose of this paper is to address the problem of content immutability and integrity of online scientific periodicals on the sites of small publishers that can be violated not only by the external hack of the publisher’s site but also by publisher’s and author’s misconduct or by submitting different versions of a periodical to different sites. Design/methodology/approach The authors defined a list of requirements that verify online scientific publications immutability and integrity. Then, the authors analyzed existing projects and recently emerged information on security technologies and identified challenges met during the development and testing. The use of the public blockchain network Ethereum as a secure storage location for data was explained. Findings The authors developed the method of checking online scientific periodicals for immutability and presented ecosystem architecture to control immutability and integrity of data. On the example of the online periodical “Istoriya”, it was demonstrated how the immutability of online scientific publication has been verified with the use of the public blockchain over a six-month period. First, operating results were evaluated; challenges hampering the implementation of the suggested ecosystem on Ethereum now were identified; and potential advantages of the suggested approach as compared to similar projects were discovered. Research limitations/implications The considered prototype is not a ready-to-use system, but in future providing higher transparency and the development of general distributed ecosystem small publishers will have new opportunities for development given that the issues of scalability, reliability and operating speed on a public blockchain will be addressed. Introduction of the described ecosystem may even provoke some changes on such conservative market as that of publishing of academic papers. Originality/value This research is one of the first attempts to expand digital object identifier technology with the use of additional verifications based on the data storage and search in the public blockchain. The suggested idea is the example of “blockchainified science” that was brought to implementation in a real online journal. This method has some advantages compared to Crossmark service.
Summary Contemporary blockchain such as Bitcoin and Ethereum execute transactions serially by miners and validators and determine the Proof‐of‐Work (PoW). Such serial execution is unable to exploit modern multi‐core 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 in‐parallel, using a leader–follower 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 re‐execute 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 leader–follower 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 100–500 transactions per block when using 6 machines in the community. Further, we achieve a peak of 5 end‐to‐end block creation speedup using a parallel miner over a serial miner.
The erratic price behavior and inefficiency in the crypto markets offer possibility to examine the behavioral aspects in cryptocurrency prices. Further, the cryptocurrency market is dominated by the retail investor providing an interesting platform to examine the impact of attention-driven trading in this particular asset class. Thus, the authors investigate the influence of investor attention in the cryptocurrency prices using the quantile causality approach. The results indicate that investors pay attention to the frequent news-making and ranked cryptos (Bitcoin and Ethereum). For newer cryptocurrencies like Ripple, investor attention influences their prices only during superior performance. The study provides evidence of attention-induced price pressure hypothesis in the prices of cryptocurrencies during expansionary phases and fear selling during poor market performance.
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
Zhengyang Wang, Li Xingzhou, Ruan Jinjin, Kou Jiaqing
Nowadays, encrypted digital currency offers a new way of secure trading and exchanging and has become increasingly important in our financial system. However, the temporal dynamics of cryptocurrencies is highly complex, and predictions are still challenging. In this study, we establish two prevailing machine learning models, fully-connected Artificial Neural Network (ANN) and the Long-Short-Term-Memory (LSTM), to predictively model the price of several popular cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Stellar Lumens (XLM), Litecoin (LTC), and Monero (XMR). We evaluate model performance and conduct sensitivity analysis to further understand our model behaviors. We find that although LSTM seems more appropriate for time sequence prediction task, ANN, in general, outrivals LSTM in our experiments. Using price information from other different cryptocurrencies for joint training and prediction could largely facilitate the prediction of BTC. Finally, the model predictive error is highly sensitive to the time scale of interest.
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.
Antonio Fernández Anta, Paul Rimba, Andrés Abeliuk, Manuel Cebrián · 8 authors
The recent rise of Bitcoin, and blockchain–its underlying technology–has ushered in a wave of other cryptocurrencies and blockchain applications. One such application is the Ethereum, a decentralized software platform that rose to prominence for enabling smart contracts and distributed applications. The Ethereum blockchain is powered by Ether, a platform-specific token used for transactions and paying for services on the network. Like in Bitcoin, miners try to solve cryptographic puzzles in order to get rewards, in this case, Ethers. However, experience has shown that the returns on solo mining have a high variance i.e. returns are unpredictable. Therefore, miners come together to form mining pools with power equivalent to the sum of the power of each member miner. This guarantees more predictable earnings, albeit likely lower, since earnings are shared among miners in the pool according to an agreed scheme.
Miners are allowed to migrate between mining pools as they deem fit which has led to a lot of dynamics between pools. This raises the question about what factors influence miner dynamics, like migrations. Can we understand these factors and their effect on the decentralization of the system? Are miners leaving, joining and changing pools mainly to maximize earnings and returns on their investments (on computing power)? Are there colluding miners or pools? As part of our efforts to answer some of these questions, we obtained data from the Ethereum blockchain system
with records of about the 60 most prominent pools in the system (which account for more than 90% of the power), their miners, reward payouts, and miner movements including those that join, leave, or switch pools. These records are broken down into time windows each of four weeks.
Initial analysis seems to suggest a preferential attachment and detachment (when miners decide
to join or leave a pool) with fairly strong correlations between pool power and the percentage of
miners migrating from and to other pools across all time windows. Furthermore, distributions of
relative miner power (inferred from the percentage of payouts they get) in some time windows seem
to follow power-law distribution while over other time windows follow a lognormal distribution.
To understand miner migrations between pools, we build a network from the payouts records with
pools as nodes and miners as edges. Hence, two pools i and j are connected with an edge of weight
n if n miners have received a payout from both pools i and j at any point in time. This network
revealed a few number of well-connected pools with lots of common miners between them. This
may indicate that these pools are the major players in the system, but a lot of common miners may
also indicate colluding or related pools, i.e. pools that are seemingly different but are operated by the same entity. Using four different network clustering/community detection methods, namely fast-greedy, walktrap, edge betweenness, and leading eigenvalues, reveal a community of about 30 pools consistently identified across 3 of the 4 community detection methods. Furthermore, pools that are known to be operated by the same entity are usually clustered in the same community.
As a continuation of this work, we expect to come up with a model that adequately explains the miner dynamics, miner attachments and detachments i.e. how they choose pools to join or leave. Furthermore, we hope to continue to explore the network aspects of this work to gain more
insights into the Ethereum ecosystem.
Stanisław Drożdż, Ludovico Minati, Paweł Oświȩcimka, Marek Stanuszek · 5 authors
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.
Recently, crowdsourcing has emerged as a new computing paradigm to solve problems that need human intrinsic, such as image annotation. However, there are two limitations in existing crowdsourcing platforms, i.e. non-transparent incentive mechanism and isolated profiles of workers, which harms the interests of both requesters and workers. Meanwhile, Blockchain technology introduces a solution to build a transparent, immutable data model in the Byzantine environment. Moreover, Blockchain systems (e.g. Ethereum) can also support the Tuning-complete script called smart contracts. Thus, we are motivated to use the feature of the transparent data model and smart contract in Blockchain to address the two limitations. Based on the proposed solutions, we have designed a Blockchain based framework which supports foundations of general crowdsourcing platforms. In addition, our framework also has following novel features: (1) it provides the transparent incentive mechanisms; (2) it supports a trusted worker's profile sharing in a cross-platform mode.