Shaun Azzopardi, Gordon J. Pace, Fernando Schapachnik
Smart contracts have been proposed as executable implementations enforcing real-life contracts. Unfortunately, the semantic gap between these allows for the smart contract to diverge from its intended deontic behaviour. In this paper we show how a deontic contract can be used for real-time monitoring of smart contracts specifically and request-based interactive systems in general, allowing for the identification of any violations. The deontic logic of actions we present takes into account the possibility of action failure (which we can observe in smart contracts), allowing us to consider novel monitorable semantics for deontic norms. For example, taking a rights-based view of permissions allows us to detect the violation of a permission when a permitted action is not allowed to succeed. A case study is presented showing this approach in action for Ethereum smart contracts.
With the explosive growth in cryptocurrencies over the last couple of years, the cost of mining these technologies (the process through which users devote CPU power to operate the underlying blockchains) have similarly exploded. This paper examines one overarching question regarding this issue â what factor or factors explain the geographic distribution of cryptocurrency nodes (mining operations) across the world? In exploring this question, this research considers electricity price, internet access, Tor network relays, and others. Using node distribution data for Bitcoin and Ethereum â the two largest cryptocurrencies â this paper analyzes cross-sectional and panel data regression models, and establishes that electricity price has not played a significant role in this distribution up to this point, and concludes that the historical association between Tor relays and Bitcoin use has had a much greater impact. Lastly, this paper discusses the broader implications of its findings, and the potential areas of research for further understanding of this field.
Blockchain technology is having a deep impact on the financial and technical sectors providing a mechanism for the creation of decentralized currencies and a number of applications in different fields.At the core of the technology there is a consensus protocol enabling the maintenance of a distributed ledger.In general current systems are complex schemes that implement a combination of cryptographic algorithm, distributed techniques, and incentive driven behaviour.In this paper we focus on three of the most diffused platforms, i.e.Bitcoin, Ripple, and Ethereum, and provide a comparative analysis of their most important features such as the architecture, the scripting language, the economic and security properties.
Blockchain is a technology to exchange digital assets including cryptocurrencies, data, software, patents etc. Blockchain is designed in various types depending on different architectures and configurations for different purposes. The mathematical and cryptographic mechanisms and consensus algorithms, implemented in the Blockchain, guarantee trust, security, integrity and availability of transactions between participants. \nThis thesis is aimed to consider mathematical foundation and algorithmic aspects of Ethereum from the base network to high level of its functionality with the comparison to mathematical mechanisms of Bitcoin. In this thesis we describe Ethereum as stack of layers. Our mission is to extract cryptographic and mathematical mechanisms implemented in each layer. \nWe present how hash function is utilized to construct cost function and Hashcash protocol. Hashcash protocol is implemented in Proof of Work (PoW) mechanism. Also, we explain how hash function is used to connect blocks in the chain of blocks and how hash function is used to organize transactions in a Merkle tree and in a Merkle-Patricia-trie. \nThis thesis describes how Elliptic Curve Digital Signature Algorithm (ECDSA) and Lamport signing algorithm provide integrity of transactions. Elliptic Curve Integrated Encryption Scheme (ECIES) is hybrid encryption scheme which is utilized to provide confidentiality of transactions. The Elliptic Curve Cryptography (ECC) is used to disguise identities in both Bitcoin and Ethereum network. The new approaches to provide privacy are Ring signatures and Zk-Snarks. \nThis thesis also explains Bloom filter structure in Ethereum. The Bloom filter is a probabilistic data structure to test membership of elements in big data bases.
A landmark security property of smart contracts is liquidity: in a non-liquid contract, it may happen that some funds remain frozen. The relevance of this issue is witnessed by a recent liquidity attack to the Ethereum Parity Wallet, which has frozen $${\sim }160M$$ USD within the contract, making this sum unredeemable by any user. We address the problem of verifying liquidity of Bitcoin contracts. Focussing on BitML, a contracts DSL with a computationally sound compiler to Bitcoin, we study various notions of liquidity. Our main result is that liquidity of BitML contracts is decidable, in all the proposed variants. To prove this, we first transform the infinite-state semantics of BitML into a finite-state one, which focusses on the behaviour of any given set of contracts, abstracting the context moves. With respect to the chosen contracts, this abstraction is sound and complete. Our decision procedure for liquidity is then based on model-checking the finite space of states of the abstraction.
Currently, Internet of Things (IoT) and blockchain technologies are experiencing exponential growth in academia and industry. Generally, IoT is a centralized system whose security and performance mainly rely on centralized servers. Therefore, users have to trust the centralized servers; in addition, it is difficult to coordinate external computing resources to improve the performance of IoT. Fortunately, the blockchain may provide this decentralization, high credibility and high security. Consequently, blockchain-based IoT may become a reasonable choice for the design of a decentralized IoT system. In this paper, we propose a novel blockchain-based threshold IoT service system: BeeKeeper. In the BeeKeeper system, servers can process a user's data by performing homomorphic computations on the data without learning anything from them. Furthermore, any node can become a leader's server if the node and the leader desire so. In this way, BeeKeeper's performance can continually increase by attracting external computing resources to join in it. Moreover, malicious nodes can be scrutinized. In addition, BeeKeeper is fault tolerant since a user's BeeKeeper protocol may work smoothly as long as a threshold number of its servers are active and honest. Finally, we deploy BeeKeeper on the Ethereum blockchain and give the corresponding performance evaluation. In our experiments, servers can generate their response with about 107 ms. Moreover, the performance of BeeKeeper mainly depends on the blockchain platform. For instance, the response time is about 22.5 s since the block interval of Ethereum blockchain is about 15 s. In fact, if we use some other blockchain with short block interval, the response time may be obviously short.
Marian Gidea, Daniel Goldsmith, Yuri Katz, Pablo Roldan · 5 authors
We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 - beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -- $k$-means clustering -- in order to automatically recognize the emerging chaotic regime in a complex system approaching a critical transition. We first test our methodology on the complex system dynamics of a Lorenz-type attractor, and then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions in the cryptocurrency markets, even though the relevant time series exhibit a highly erratic behavior.
In recent publications, we presented a novel formal symbolic process virtual machine (FSPVM) framework that combined higher-order theorem proving and symbolic execution for verifying the reliability and security of smart contracts developed in the Ethereum blockchain system without suffering the standard issues surrounding reusability, consistency, and automation. A specific FSPVM, denoted as FSPVM-E, was developed in Coq based on a general, extensible, and reusable formal memory (GERM) framework, an extensible and universal formal intermediate programming language, denoted as Lolisa, which is a large subset of the Solidity programming language that uses generalized algebraic datatypes, and a corresponding formally verified interpreter for Lolisa, denoted as FEther, which serves as a crucial component of FSPVM-E. However, our past work has demonstrated that the execution efficiency of the standard development of FEther is extremely low. As a result, FSPVM-E fails to achieve its expected verification effect. The present work addresses this issue by first identifying three root causes of the low execution efficiency of formal interpreters. We then build abstract models of these causes, and present respective optimization schemes for rectifying the identified conditions. Finally, we apply these optimization schemes to FEther, and demonstrate that its execution efficiency has been improved significantly.
This paper reports on the development and verification of a novel formal symbolic process virtual machine (FSPVM) for verifying the reliability and security of Ethereum smart contracts, denoted as FSPVM-E, completely in Coq proof assistant. It adopts execution-verification isomorphism (EVI), an extension of Curry-Howard isomorphism (CHI), as its fundamental theoretical framework. The current version of FSPVM-E is constructed on a general, extensible, and reusable formal memory (GERM) framework, an extensible and universal formal intermediate programming language Lolisa, which is a large subset of the Solidity programming language using generalized algebraic datatypes, and the corresponding formally verified interpreter of Lolisa, denoted as FEther. It supports the ERC20 standard and can automatically simultaneously symbolically execute the smart contract programs of Ethereum and verify their reliability and security properties using Hoare logic in Coq. In addition, this work, contributes to solving the problems of automation, inconsistency and reusability in higher-order logic theorem proving.
At the beginning of 2018, there is a growing belief that blockchain technologies constitute a revolutionary innovation in how we transfer value electronically. In that vein, blockchain may be a suitable complement to ontologies to achieve a big part of the vision of the semantic Web by Tim Berners-Lee. We believe that if this complementarity is to be achieved blockchain and ontologies must co-evolve. In this paper, we focus on what and how to engineer models, methods, designs, and implementations for this co-evolution. As a first step in this co-evolution, we propose a conceptual design of a governance ontology represented as meta-data tags to be embedded and instantiated in a smart contract at the blockchain protocol level. We develop this design by examining and analyzing smart contracts from the infamous The DAO experiment on the Ethereum blockchain. We believe there are two contributions of this paper: it serves to inform and implore the blockchain and ontology communities to recognize and collaborate with each other; and it outlines a roadmap for engineering artifacts to bridge the gap between blockchain community focus on protocol-level blockchain interoperability and the ontology community focus on semantic-level interoperability.
Purpose This paper aims to present a methodology for constructing cointegrated portfolios consisting of different cryptocurrencies and examines the performance of a number of trading strategies for the cryptocurrency portfolios. Design/methodology/approach The authors apply a series of statistical methods, including the Johansen test and Engle–Granger test, to derive a linear combination of cryptocurrencies that form a mean-reverting portfolio. Trading systems are designed and different trading strategies with stop-loss constraints are tested and compared according to a set of performance metrics. Findings The paper finds cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH) and Litecoin (LTC), and the corresponding trading strategies are shown to be profitable under different configurations. Originality/value The main contributions of the study are the use of multiple altcoins in addition to bitcoin to construct a cointegrated portfolio, and the detailed comparison of the performance of different trading strategies with and without stop-loss constraints.
In this paper, we study the contemporaneous tail dependence structure in a pairwise comparison of the ten largest cryptocurrencies, namely Bitcoin, Dash, Dogecoin, Ethereum, Litecoin, Monero, Namecoin, Novacoin, Peercoin, and Ripple. We apply multivariate extreme value theory and we estimate a bias-corrected extreme correlation coefficient. Our findings reveal clear patterns of significantly high bivariate dependency in the distribution tails of some of the most basic and widespread cryptocurrencies, primarily over various downside constraints. This means that extreme correlation is not related to cryptocurrency market volatility per se, but to the trend of the cryptocurrency market. Therefore, extreme correlation increases in bear markets, but not in bull markets for these pairs. Interestingly, there is also a significant number of pairs which exhibit a weak level of dependency in distribution tails.
Interacting with Ethereum smart contracts can have potentially devastating financial consequences. In light of this, several regulatory bodies have called for a need to audit smart contracts for security and correctness guarantees. Unfortunately, auditing smart contracts that do not have readily available source code can be challenging, and there are currently few tools available that aid in this process. Such contracts remain opaque to auditors. To address this, we present Erays, a reverse engineering tool for smart contracts without the need for source code. Erays takes in smart contract from the Ethereum blockchain and produces high-level pseudocode suitable for manual analysis. We show how Erays can be used to provide insight into several contract properties, such as code complexity and code reuse in the ecosystem. We then leverage Erays to link contracts with no previously available source code to public source code, thus reducing the overall opacity in the ecosystem. Finally, we demonstrate how Erays can be used for reverse-engineering in four case studies: high-value multi-signature wallets, arbitrage bots, exchange accounts, and finally, a popular smart-contract game, Cryptokitties. We conclude with a discussion regarding the value of reverse engineering in the smart contract ecosystem, and how Erays can be leveraged to address the challenges that lie ahead.
There is an immense need of a proof of delivery (PoD) of today's digital media and content, especially those that are subject to payment. Current PoD systems are mostly centralized and heavily dependent on a trusted third party (TTP) especially for payment. Such existing PoD systems often lack security, transparency, and visibility, and are not highly credible, as the TTP can be subject to failure, manipulation, corruption, compromise, and hacking. In this paper, we propose a decentralized PoD solution for PoD of digital assets. Our solution leverages key features of blockchain and Ethereum smart contracts to provide immutable and tamper-proof logs, accountability, and traceability. Ethereum smart contracts are used to orchestrate and govern all interactions and transactions including automatic payments in Ether cryptocurrency between customers, digital-content provider, and the file server hosting the digital content. All entities are incentivized to act honestly, and our solution has a mechanism to handle dispute if arisen among participants. The solution has an off-chain secure download phase involving the file server and customers. Moreover, our solution leverages the benefits of interplanetary file system to store the agreed upon terms and conditions between the smart contract actors. A security analysis of our proposed system has been provided. The full code of the smart contract has been publicly made available on Github.
Open access
Blockchain Technology Applications and Security
FinTech, Crowdfunding, Digital Finance
Advanced Steganography and Watermarking Techniques
The role-based access control (RBAC) framework is a mechanism that describes the access control principle. As a common interaction, an organization provides a service to a user who owns a certain role that was issued by a different organization. Such trans-organizational RBAC is common in face-toface communication but not in a computer network, because it is difficult to establish both the security that prohibits the malicious impersonation of roles and the flexibility that allows small organizations to participate and users to fully control their own roles. In this paper, we present an RBAC using smart contract (RBAC-SC), a platform that makes use of Ethereum's smart contract technology to realize a trans organizational utilization of roles. Ethereum is an open blockchain platform that is designed to be secure, adaptable, and flexible. It pioneered smart contracts, which are decentralized applications that serve as “autonomous agents”running exactly as programmed and are deployed on a blockchain. The RBAC-SC uses smart contracts and blockchain technology as versatile infrastructures to represent the trust and endorsement relationship that are essential in the RBAC and to realize a challenge-response authentication protocol that verifies a user's ownership of roles. We describe the RBAC-SC framework, which is composed of two main parts, namely, the smart contract and the challenge-response protocol, and present a performance analysis. A prototype of the smart contract is created and deployed on Ethereum's Testnet blockchain, and the source code is publicly available.
A smart contract is hard to patch for bugs once it is deployed, irrespective of the money it holds. A recent bug caused losses worth around $50 million of cryptocurrency. We present ZEUS-a framework to verify the correctness and validate the fairness of smart contracts. We consider correctness as adherence to safe programming practices, while fairness is adherence to agreed upon higher-level business logic. ZEUS leverages both abstract interpretation and symbolic model checking, along with the power of constrained horn clauses to quickly verify contracts for safety. We have built a prototype of ZEUS for Ethereum and Fabric blockchain platforms, and evaluated it with over 22.4K smart contracts. Our evaluation indicates that about 94.6% of contracts (containing cryptocurrency worth more than $0.5 billion) are vulnerable. ZEUS is sound with zero false negatives and has a low false positive rate, with an order of magnitude improvement in analysis time as compared to prior art.
Pedro Bação, António Portugal Duarte, Hélder Sebastião, Srdjan Redžepagić
This paper investigates the information transmission between the most important cryptocurrencies -Bitcoin, Litecoin, Ripple, Ethereum and Bitcoin Cash. We use a VAR modelling approach, upon which the Geweke’s feedback measures and generalized impulse response functions are computed. This methodology allows us to fully characterize the direction, intensity and persistence of information flows between cryptocurrencies. At the availabledata granularity, most of information transmission is contemporaneous, that is, it occurs within a day. However, it seems that there are some lagged feedback effects, mainly from other cryptocurrencies to Bitcoin. The generalized impulse-response functions confirm that there is a strong contemporaneous correlation and that there is not much evidence of lagged effects. The exception appears to be related to the overreaction of Bitcoin returns to contemporaneous shocks
Leopoldo Catania, Stefano Grassi, Francesco Ravazzolo
Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regulation and their market far from being “efficient”, require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evolution of the cryptocurrencies’ volatility has been found to exhibit at the same time differences and similarities with other financial time-series, e.g. foreign exchanges returns. This short note focuses on predicting the conditional volatility of the four most traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investigate the effect of accounting for long memory in the volatility process as well as its asymmetric reaction to past values of the series to predict: 1 day, 1 and 2 weeks volatility levels.
This paper aims to select the best model or set of models for modelling volatility of the four most popular cryptocurrencies, i.e. Bitcoin, Ethereum, Ripple and Litecoin. More than 1000 GARCH models are fitted to the log returns of the exchange rates of each of these cryptocurrencies to estimate a one-step ahead prediction of Value-at-Risk (VaR) and Expected Shortfall (ES) on a rolling window basis. The best model or superior set of models is then chosen by backtesting VaR and ES as well as using a Model Confidence Set (MCS) procedure for their loss functions. The results imply that using standard GARCH models may yield incorrect VaR and ES predictions, and hence result in ineffective risk-management, portfolio optimisation, pricing of derivative securities etc. These could be improved by using instead the model specifications allowing for asymmetries and regime switching suggested by our analysis, from which both investors and regulators can benefit.
Jethin Abraham, Daniel Higdon, John B. Nelson, Juan G. Ibarra
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over \$160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict the direction of price changes. By utilizing this model, a person is able to make better informed purchase and selling decisions related to Bitcoin and Ethereum.