The advent of quantum computing threatens blockchain protocols and networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH, will be no longer secure. Quantum computers will be able to break them within a short period of time. Similarly, Grover's algorithm concedes a quadratic advantage for mining blocks in certain consensus protocols such as proof of work. Today, there are hundreds of billions of dollars denominated in cryptocurrencies and other digital assets that rely on blockchain ledgers as well as thousands of blockchain-based applications storing value in blockchain networks. Cryptocurrencies and blockchain-based applications require solutions that guarantee quantum resistance in order to preserve the integrity of data and assets in these public and immutable ledgers. The quantum threat and some potential solutions are well understood and presented in the literature. However, most proposals are theoretical, require large QKD networks, or propose new quantum-resistant blockchain networks to be built from scratch. Our work, which is presented in this paper, is pioneer in proposing an end-to-end framework for post-quantum blockchain networks that can be applied to existing blockchain to achieve quantum-resistance. We have developed an open-source implementation in an Ethereum-based (i.e., EVM compatible) network that can be extended to other existing blockchains. For the implementation we have (i) used quantum entropy to generate post-quantum key pairs, (ii) established post-quantum TLS connections and X.509 certificates to secure the exchange of information between blockchain nodes over the internet without needing a large QKD network, (iii) introduced a post-quantum second signature in transactions using Falcon-512 post-quantum keys, and (iv) developed the first on-chain verification of post-quantum signatures using three different mechanisms that are compared and analyzed: Solidity smart-contracts run by the validators for each transaction, modified EVM Opcode, and precompiled smart contracts.
We considered scalable anonymous voting on the Ethereum blockchain. We identified three major bottlenecks in implementation: (1) division overflow in encryption of voting values for anonymity; (2) large time complexity in tallying, which limited scalability in the number of candidates and voters; and (3) tallying failure due to "no votes" from registered voters. Previous schemes failed at tallying if one (or more) registered voters did not send encrypted voting values. Algorithmic solutions and implementation details are provided. An experiment using Truffle and Remix running on a desktop PC was performed for evaluation. Our scheme shows great reduction in gas, which measures the computational burden of smart contracts to be executed on Ethereum. For instance, our scheme consumed 1/53 of the gas compared to a state-of-the-art solution for 60 voters. Time complexity analysis shows that our scheme is asymptotically superior to known solutions. In addition, we propose a solution to the tallying failure due to the "no vote" from registered voters.
The worth of digital currencies is increasing due to its proposed advantages and profits. Though decentralized, these digital currencies can be bought with digital wallets using cryptocurrency platform. Efficient Market Hypothesis (EMH) suggests fundamentals for understanding of financial markets however the opponents believe that this theory is incompetent in explaining the functioning of the markets. EMH is not a perfect model nevertheless it provides a concrete base for the analysis of capital markets. EMH’s weak version is utilized for this study. This research compares three top cryptocurrencies- Bitcoin, Ethereum and Litecoin to analyze their long-range memory effect to check the market efficiency and also to estimate the volatility for further investments in different cryptocurrencies. Generalized Hurst exponent methodology is applied to examine long range memory in selected cryptocurrencies market. Daily data from 17th September 2015 till 17th October 2018 is used in this study. It was found that: (i) Long memory exists in the selected cryptocurrencies; (ii) Ethereum market is more persistent than Bitcoin and Litecoin as its Hurst exponent is more than the other cryptocurrencies. These findings can be a source of assistance for the policy makers and investors while making prudent decisions regarding investment in emerging cryptocurrencies market.
Dorcas Ofori-Boateng, Ignacio Segovia Dominguez, Murat Kantarcioglu, Cuneyt G. Akcora · 5 authors
Motivated by the recent surge of criminal activities with cross-cryptocurrency trades, we introduce a new topological perspective to structural anomaly detection in dynamic multilayer networks. We postulate that anomalies in the underlying blockchain transaction graph that are composed of multiple layers are likely to also be manifested in anomalous patterns of the network shape properties. As such, we invoke the machinery of clique persistent homology on graphs to systematically and efficiently track evolution of the network shape and, as a result, to detect changes in the underlying network topology and geometry. We develop a new persistence summary for multilayer networks, called stacked persistence diagram, and prove its stability under input data perturbations. We validate our new topological anomaly detection framework in application to dynamic multilayer networks from the Ethereum Blockchain and the Ripple Credit Network, and demonstrate that our stacked PD approach substantially outperforms state-of-art techniques.
The insurance industry is heavily dependent on several processes executed among multiple entities, such as insurer, insured, and third-party services. The increasingly competitive environment is pushing insurance companies to use advanced technologies to address multiple challenges, namely lack of trust, lack of transparency, and economic instability. To this end, blockchain is used as an emerging technology that enables transparent and secure data storage and transmission. In this paper, we propose CioSy, a collaborative blockchain-based insurance system for monitoring and processing the insurance transactions. To the best of our knowledge, the existing approaches do not consider collaborative insurance to achieve an automated, transparent, and tamper-proof solution. CioSy aims at automating the insurance policy processing, claim handling, and payment using smart contracts. For validation purposes, an experimental prototype is developed on Ethereum blockchain. Our experimental results show that the proposed approach is both feasible and economical in terms of time and cost.
Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include "on-chain" from the pool of all pending transactions. This paper investigates the problem of designing a blockchain transaction fee mechanism through the lens of mechanism design. We introduce two new forms of incentive-compatibility that capture some of the idiosyncrasies of the blockchain setting, one (MMIC) that protects against deviations by profit-maximizing miners and one (OCA-proofness) that protects against off-chain collusion between miners and users. This study is immediately applicable to a recent (August 5, 2021) and major change to Ethereum's transaction fee mechanism, based on a proposal called "EIP-1559." Historically, Ethereum's transaction fee mechanism was a first-price (pay-as-bid) auction. EIP-1559 suggested making several tightly coupled changes, including the introduction of variable-size blocks, a history-dependent reserve price, and the burning of a significant portion of the transaction fees. We prove that this new mechanism earns an impressive report card: it satisfies the MMIC and OCA-proofness conditions, and is also dominant-strategy incentive compatible (DSIC) except when there is a sudden demand spike. We also introduce an alternative design, the "tipless mechanism," which offers an incomparable slate of incentive-compatibility guarantees -- it is MMIC and DSIC, and OCA-proof unless in the midst of a demand spike.
Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets. While this setting ensures some level of privacy, it has been shown that, even when data is not directly shared, the training process is vulnerable to privacy attacks including data reconstruction and model inversion attacks. Additionally, malicious agents that train on inverted labels or random data, may arbitrarily weaken the accuracy of the global model. This paper addresses these challenges and presents Privacy-preserving and trustable Distributed Learning (PT-DL), a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protections of the agents' data, and Ethereum smart contracts to ensure trustability. The paper shows that PT-DL is resilient up to a 50% collusion attack, with high probability, in a malicious trust model and the experimental evaluation illustrates the benefits of the proposed model as a privacy-preserving and trustable distributed multi-agent learning system on several classification tasks.
Uri Kirstein, Shelly Grossman, Michael Mirkin, James Wilcox · 6 authors
An attacker that gains access to a cryptocurrency user's private keys can perform any operation in her stead. Due to the decentralized nature of most cryptocurrencies, no entity can revert those operations. This is a central challenge for decentralized systems, illustrated by numerous high-profile heists. Vault contracts reduce this risk by introducing artificial delay on operations, allowing abortion by the contract owner during the delay. However, the theft of a key still renders the vault unusable and puts funds at risk. We introduce Phoenix, a novel contract architecture that allows the user to restore its security properties after key loss. Phoenix takes advantage of users' ability to store keys in easily-available but less secure storage (tier-two) as well as more secure storage that is harder to access (tier-one). Unlike previous solutions, the user can restore Phoenix security after the theft of tier-two keys and does not lose funds despite losing keys in either tier. Phoenix also introduces a mechanism to reduce the damage an attacker can cause in case of a tier-one compromise. We formally specify Phoenix's required behavior and provide a prototype implementation of Phoenix as an Ethereum contract. Since such an implementation is highly sensitive and vulnerable to subtle bugs, we apply a formal verification tool to prove specific code properties and identify faults. We highlight a bug identified by the tool that could be exploited by an attacker to compromise Phoenix. After fixing the bug, the tool proved the low-level executable code's correctness.
Manuel M. T. Chakravarty, Nikos Karayannidis, Aggelos Kiayias, Michael Peyton Jones · 5 authors
Custom currencies (ERC-20) on Ethereum are wildly popular, but they are second class to the primary currency Ether. Custom currencies are more complex and more expensive to handle than the primary currency as their accounting is not natively performed by the underlying ledger, but instead in user-defined contract code. Furthermore, and quite importantly, transaction fees can only be paid in Ether. In this paper, we focus on being able to pay transaction fees in custom currencies. We achieve this by way of a mechanism permitting short term liabilities to pay transaction fees in conjunction with offers of custom currencies to compensate for those liabilities. This enables block producers to accept custom currencies in exchange for settling liabilities of transactions that they process. We present formal ledger rules to handle liabilities together with the concept of babel fees to pay transaction fees in custom currencies. We also discuss how clients can determine what fees they have to pay, and we present a solution to the knapsack problem variant that block producers have to solve in the presence of babel fees to optimise their profits.
Saha Reno, Sheikh Surfuddin Reza Ali CHOWDHURY, Iqramuzzaman SADI
Lending systems in real world are not much secure and reliable as the borrower and third parties involved in this aspect may create various deceitful situations. Blockchain is a secure system where the utilization of smart contract can avoid deceptive phenomena involved in lending but the decline in exchange rate of cryptocurrency can create the opportunity to pay back less than the borrowed amount in terms of fiat money. In this paper, a blockchain and smart contract-based lending framework is designed which requires the borrower to provide Ethereum Request for Comments (ERC)-20 standard tokens as collateral to mitigate the associated risks. The smart contract feature is utilized to automate the system without any third-party management. Besides, transaction stored in the blocks creates transparency among the users of the system. To tackle the aforementioned issues, ERC-20 token value is increased periodically and the instability of the exchange rate is surveilled by the system. By the end of this paper, some test cases and charts relevant to the data set are evaluated to assess the effectiveness of the system.
Goldfinger-Attacken zielen darauf ab, den Wert einer Ziel-Kryptowährung zum Absturz zu bringen, indem die Mehrheit der Stimmrechte im System genutzt wird das zu Grunde liegende Konsensprotokoll untergraben. In einem Proof-of-Stake-Kontext, in dem die Stimmkraft auf der Menge der gehaltenen Kryptowährung basiert, kann dies in Form eines Buy-out-Angriffs erreicht werden, bei dem eine Mehrheit der Zielwährung gekauft wird. In diesem Zusammenhang wurde der Race to the Door (RTTD)-Effekt beschrieben, der dazu führt, dass immer mehr Inhaber aus der Zielwährung aussteigen, bevor diese wertlos wird. Dieser Effekt senkt den Preis für weitere Stimmenanteile, wodurch der Angriff billiger wird, je weiter er fortschreitet. Diese Arbeit soll zeigen, dass ein Angriff im Stil von Race to the Door auch in einem Proof-of-Work (PoW)-Kontext technisch möglich ist, ohne eine Mehrheit der Stimmrechte (d. h. der Hash-Rate) zu erlangen. Zu diesem Zweck werden die technische Machbarkeit und die Kosten eines solchen Angriffs am Beispiel von Ethereum untersucht. Zunächst wird ein Systemmodell für RTTD-Angriffe auf PoW-basierte Kryptowährungen vorgestellt, um einen Überblick zu geben. Der Angriff wird dabei in die Phasen Vorbereitung, Rennen und Angriff unterteilt. Um die technische Machbarkeit zu demonstrieren, werden diese Phasen in Form von Smart Contracts auf Ethereum umgesetzt. Für die Angriffsphase werden drei Varianten vorgestellt, die jeweils einen unterschiedlichen Denial-of-Service-Angriff realisieren. Dazu werden In-Band-Zahlungen genutzt, um entweder das Auslösen zusätzlicher Transaktionen oder die Erzeugung leerer Blöcke durch Miner zu incentivieren. Um die Kosten der vorgeschlagenen Angriffsvarianten abzuschätzen, wird eine empirische Analyse durchgeführt, bei der Transaktionsdaten von historischen Überlastungsphasen der Ethereum-Blockchain untersucht werden. Anhand der Ergebnisse werden die Kosten der Angriffsvarianten geschätzt und verglichen. Die stündlichen Kosten für das Blockieren von Transaktionen durch das auslösen weiterer Transaktionen betragen etwa 870 Ether. Die Incentivierung von Minern, die ein Drittel der Blöcke leer lassen, kostet etwa 790 Ether pro Stunde. Die Arbeit zeigt, dass Race to the Door-Attacken auch im Kontext von PoW-basierten Kryptowährungen technisch durchführbar sind. Die Kosten des Angriffs hängen dabei von seiner Intensität und Dauer ab. Die Intensität kann in der Angriffsphase konfiguriert werden und die Dauer hängt von der für den Angriff verfügbaren Geldmenge ab.
Smart contracts present a uniform approach for deploying distributed computation and have become a popular means to develop security critical applications. A major barrier to adoption for many applications is the public nature of existing systems, such as Ethereum. Several systems satisfying various definitions of privacy and requiring various trust assumptions have been proposed; however, none achieved the universality and uniformity that Ethereum achieved for non-private contracts: One unified method to construct most contracts. We provide a unified security model for private smart contracts which is based on the Universal Composition (UC) model and propose a novel core protocol, KACHINA, for deploying privacy-preserving smart contracts, which encompasses previous systems. We demonstrate the KACHINA method of smart contract development, using it to construct a contract that implements privacy-preserving payments, along the lines of Zerocash, which is provably secure in the UC setting and facilitates concurrency.
Matthieu Nadini, Laura Alessandretti, Flavio Di Giacinto, Mauro Martino · 6 authors
Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.
Possible scenarios for using blockchain technology in the field of education are considered. Methods and technologies of tokenization of assets, related to the educational process, are investigated. It is concluded, that the blockchain technology is decentralized and transparent with a high degree of reliability, which ensures the equality of all users of the chain's services. The transparency of the technology guarantees the participants in the process against abuse and forgery of documents. The study of the features of smart contracts made it possible to form the advantages of smart contracts in the field of education. This is, first of all, the conclusion of agreements without the participation of third parties, as well as the security and confidentiality of agreements. This ensures that the terms and subject of the agreement are kept secret, and that no one else can amend the agreement. At the same time, storing the contract in encrypted form ensures its confidentiality. A decrease in the cost of operations was noted. Tokenization of educational assets is considered on the example of preparing and defending a thesis with the subsequent registration of a diploma. The processes of passing the thesis in the context of using blockchain technology and issuing smart contracts are considered in detail. The advantages of using blockchain technology and smart contracts are illustrated with specific examples. A study of the means and mechanisms for ensuring the confirmation of the authenticity of educational documents, the confidentiality of students' personal cards, and student identification has been carried out. To create a decentralized distributed ledger for tokenization of educational assets, it is proposed to use blockchain technology and smart contracts based on the Ethereum platform
With the development of blockchain technologies, the number of smart contracts deployed on blockchain platforms is growing exponentially, which makes it difficult for users to find desired services by manual screening. The automatic classification of smart contracts can provide blockchain users with keyword-based contract searching and helps to manage smart contracts effectively. Current research on smart contract classification focuses on Natural Language Processing (NLP) solutions which are based on contract source code. However, more than 94% of smart contracts are not open-source, so the application scenarios of NLP methods are very limited. Meanwhile, NLP models are vulnerable to adversarial attacks. This paper proposes a classification model based on features from contract bytecode instead of source code to solve these problems. We also use feature selection and ensemble learning to optimize the model. Our experimental studies on over 11K real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models. Our model also has good resistance to adversarial attacks compared with NLP-based models. In addition, our analysis reveals that account features used in many smart contract classification models have little effect on classification and can be excluded.
Decentralized systems and protocols are becoming more popular every year. The main limitations of public permissionless blockchains are low bandwidth and high fees. There are various ways to reduce the load on the network to address these drawbacks, the majority of them were developed for Ethereum but stay valid for many other chains. The paper considers the following options for scaling decentralized systems: consensus modification and sharding that are embedded in the core of a blockchain, second layer solutions and sidechains that can be built on top of a network.
Bütün sektörler dahilinde finans sektöründe de müşterilere ait fikir ve düşüncelerinin belirlenmesi, firma ve kurumların ileriki dönemler için sunacağı hizmetleri etkilemektedir. Kripto para birimlerinin (Bitcoin, Ethereum, Ripple vb.) ekonomik ve sosyal etkileri hızla artmaya devam ettikçe, ilgili haber makalelerinin ve sosyal medya yayınlarının, özellikle de tweetlerin yaygınlığı da artmaktadır. Bu çalışmada, Twitter kullanıcılarının finans sektörü konularından biri olan Bitcoin ile ilgili yorumları derlenerek bir duygu analizi çalışması yapılmıştır. Kullanıcı yorumları, Twitter’ın sunmuş olduğu API hizmeti vasıtasıyla Python Programlama Dili kullanılarak alınmış; yorumlar olumlu, nötr ve olumsuz etiketler ile ayrıştırılmış, etiket bulutunda toplanmıştır. Naïve Bayes ve Lojistik Regresyon algoritmaları kullanılarak oluşturulan modellerde başarı oranları karşılaştırılmıştır. Naïve Bayes uygulamasının tweetlerin duygularını tahmin etmedeki başarı oranı %72,19 olurken, Lojistik Regresyon uygulamasında bu oran %75,53 olmuştur. Çalışmanın ikinci aşamasında ise, duygu analizinden sonra “Bitcoin” anahtar kelimesi içeren günlük pozitif tweet oranı ile Bitcoin günlük açılış değeri beraber kullanılarak Bitcoin kapanış değeri tahminlemesi yapılmıştır. Finans verileri Yahoo Finance web sitesi üzerinden alınmış; Doğrusal Regresyon ve Rastgele Orman Regresyon yöntemleri ile modeller oluşturulmuştur. Doğrusal Regresyon için r² değeri %88,97 çıkarken, Rastgele Orman Regresyonu için ise %94,16 olmuştur.Anahtar Kelimeler: Duygu analizi, Twitter, Bitcoin, Makine öğrenmesi, Veri madenciliği, Finans
One of the most important discoveries and creative developments that is playing a vital role in the professional world today is blockchain technology. A blockchain is a distributed, digitized and consensus-based secure information storage mechanism. Blockchain technology moves in the direction of persistent revolution and change. In the last couple of years, the upsurge in blockchain technology has obliged scholars and specialists to scrutinize new ways to apply blockchain technology with a wide range of domains. The dramatic increase in blockchain technology has provided many new application opportunities, including e-voting application. The present article provides a systematic review of emerging blockchain-based e-voting systems. In this paper, we call attention to the open research matters in this fast-growing field, explaining them in some details. It was concluded that frameworks needed enhancements in order to be used in voting systems due to these reservations. KEYWORDS: blockchain, e-voting, cloud computing, ethereum; ballot
William Hughes, Alejandro Russo, Gerardo Schneider
Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a middleman. On-chain execution is expensive, with miners charging fees for distributed execution according to a cost model defined in the protocol. In particular, transactions have a high fixed cost. In this paper we present MultiCall, an interpreter that reduces the cost of smart contract execution by emulating sequences of transactions from multiple users in one transaction. We have implemented and integrated MultiCall into Ethereum. Our evaluation shows that using MultiCall provides a saving between 56.8% and 98.9% of the fixed per-transaction cost compared to the standard approach of sending transactions individually.
To date, cryptocurrency prices are volatile and many cryptocurrency developers have adopted ad hoc approaches to stabilize their cryptocurrency price. When these currencies are not 100% backed by other valued assets, part of their price volatility may arise from self-fulfilling expectations of a speculative attack (as in Obstfeld (1996)). We show that an exchange rate policy, which is less than 100% backed and dynamically adjusts in response to traders’ conversion demand eliminates speculative attacks while, under some conditions, preserving much of the desired exchange rate stability. This dynamic exchange rate policy admits a great deal of discretion to and requires commitment by the party implementing the policy. We demonstrate how to implement this policy using the Ethereum network—a smart contract blockchain environment—and how this implementation yields commitment to the policy.
Periklis Kostamis, Andreas Sendros, Pavlos S. Efraimidis
The cost of using a blockchain infrastructure as well as the time required to search and retrieve information from it must be considered when designing a decentralized application. In this work, we examine a comprehensive set of data management approaches for Ethereum applications and assess the associated cost in gas as well as the retrieval performance. More precisely, we analyze the storage and retrieval of various-sized data, utilizing smart contract storage. In addition, we study hybrid approaches by using IPFS and Swarm as storage platforms along with Ethereum as a timestamping proof mechanism. Such schemes are especially effective when large chunks of data have to be managed. Moreover, we present methods for low-cost data handling in Ethereum, namely the event-logs, the transaction payload, and the almost surprising exploitation of unused function arguments. Finally, we evaluate these methods on a comprehensive set of experiments.
Smart contract is the building block of blockchain systems that enables automated peer-to-peer transactions and decentralized services. With the increasing popularity of smart contracts, blockchain systems, in particular Ethereum, have been the "paradise" of versatile fraud activities in which Ponzi, Honeypot and Phishing are the prominent ones. Formal verification and symbolic analysis have been employed to combat these destructive scams by analyzing the codes and function calls, yet the vulnerability of each \emph{individual} scam should be predefined discreetly. In this work, we present SCSGuard, a novel deep learning scam detection framework that harnesses the automatically extractable bytecodes of smart contracts as their new features. We design a GRU network with attention mechanism to learn from the \emph{N-gram bytecode} patterns, and determines whether a smart contract is fraudulent or not. Our framework is advantageous over the baseline algorithms in three aspects. Firstly, SCSGuard provides a unified solution to different scam genres, thus relieving the need of code analysis skills. Secondly, the inference of SCSGuard is faster than the code analysis by several order of magnitudes. Thirdly, experimental results manifest that SCSGuard achieves high accuracy (0.92$\sim$0.94), precision (0.94$\sim$0.96\%) and recall (0.97$\sim$0.98) for both Ponzi and Honeypot scams under similar settings, and is potentially useful to detect new Phishing smart contracts.