Can cryptocurrencies price variations be explained by exogenous classical market prices? We evaluate this issue by using daily data on some of the most important asset prices and indexes in Thailand i.e. Gold, Oil, SET50 index, Tourism index, Mutual fund, and THB/USD exchange rate in comparison with digital asset prices i.e. Bitcoin, Ethereum, Litecoin, Ripple, DASH, and Stellar. By performing both direct and inverse relationships using correlation matrix to find distance relationship and using minimum spanning tree to find the closest path between assets, we found strong direct relationship among cryptocurrencies in digital market with SET50 index and oil price in classical markets. We also found that THB-USD exchange rate has inverse relationship with Bitcoin price, SET50 index and oil price. There is a link between cryptocurrencies asset price and some classical assets' market price.
A tourism serious game requires a transaction system to handle the transaction process's visualization between players. In this paper, we propose a serious game with a blockchain-based multiplayer transaction system. To integrate the transaction system with the game engine, we use the ethereum platform. In this serious game, ethereum handles the simulation of transaction activities between several characters' choices, including tourists, ticket sellers, jeep drivers, traders, and horse rental. The experimental results show that the gas price variable affects the speed of the transaction process. Meanwhile, setting the gas limit value in this serious game affects the transaction process's success rate between players.
Graph convolutional networks (GCNs) have been employed as a kind of significant tool on many graph-based applications recently. Inspired by convolutional neural networks (CNNs), GCNs generate the embeddings of nodes by aggregating the information of their neighbors layer by layer. However, the high computational and memory cost of GCNs due to the recursive neighborhood expansion across GCN layers makes it infeasible for training on large graphs. To tackle this issue, several sampling methods during the process of information aggregation have been proposed to train GCNs in a mini-batch Stochastic Gradient Descent (SGD) manner. Nevertheless, these sampling strategies sometimes bring concerns about insufficient information collection, which may hinder the learning performance in terms of accuracy and convergence. To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers. Then, a degree-based sampling strategy, which avoids the exponential complexity, is constructed for sampling a fixed number of nodes. Combining the above two mechanisms, the proposed model, named Mix-grained GCN (MG-GCN) achieves state-of-the-art performance in terms of accuracy, training speed, convergence speed, and memory cost through a comprehensive set of experiments on four commonly used benchmark datasets and a new Ethereum dataset.
Sina Rafati Niya, Benjamin Jeffrey, Burkhard Stiller
The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices' SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards.
Open access
2 source records
Physical Unclonable Functions (PUFs) and Hardware Security
Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.
Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach
Federated Learning harnesses data from multiple sources to build a single model. While the initial model might belong solely to the actor bringing it to the network for training, determining the ownership of the trained model resulting from Federated Learning remains an open question. In this paper we explore how Blockchains (in particular Ethereum) can be used to determine the evolving ownership of a model trained with Federated Learning. Firstly, we use the step-by-step evaluation metric to assess the relative contributivities of participants in a Federated Learning process. Next, we introduce 2CP, a framework comprising two novel protocols for Blockchained Federated Learning, which both reward contributors with shares in the final model based on their relative contributivity. The Crowdsource Protocol allows an actor to bring a model forward for training, and use their own data to evaluate the contributions made to it. Potential trainers are guaranteed a fair share of the resulting model, even in a trustless setting. The Consortium Protocol gives trainers the same guarantee even when no party owns the initial model and no evaluator is available. We conduct experiments with the MNIST dataset that reveal sound contributivity scores resulting from both Protocols by rewarding larger datasets with greater shares in the model. Our experiments also showed the necessity to pair 2CP with a robust model aggregation mechanism to discard low quality inputs coming from model poisoning attacks.
Many empirical software engineering studies show that there is a need for repositories where source codes are acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search for Ethereum blockchain data such as transactions, addresses, tokens, smart contractsâ source codes, prices and other activities taking place on the Ethereum blockchain. Despite the availability of this kind of service, retrieving specific information useful to empirical software engineering studies, such as the study of smart contractsâ software metrics, might require many subtasks, such as searching for specific transactions in a block, parsing files in HTML format, and filtering the smart contracts to remove duplicated code or unused smart contracts. In this paper, we afford this problem by creating Smart Corpus, a corpus of smart contracts in an organized, reasoned and up-to-date repository where Solidity source code and other metadata about Ethereum smart contracts can easily and systematically be retrieved. We present Smart Corpusâs design and its initial implementation, and we show how the data set of smart contractsâ source codes in a variety of programming languages can be queried and processed to get useful information on smart contracts and their software metrics. Smart Corpus aims to create a smart-contract repository where smart-contract data (source code, application binary interface (ABI) and byte code) are freely and immediately available and are classified based on the main software metrics identified in the scientific literature. Smart contractsâ source codes have been validated by EtherScan, and each contract comes with its own associated software metrics as computed by the freely available software PASO. Moreover, Smart Corpus can be easily extended as the number of new smart contracts increases day by day.
In this study, we initiate a cyberinfrastructure solution by synergizing both the blockchain and Internet of Things (IoT) technologies for transportation insurance. The insurance premium related services are encapsulated in âon-chainâ chaincodes to perform over the facts on vehicle's trip and driver's behavior, which are deduced through âoff-chainâ analytic services using the sensing data collected from vehicles' on-board sensors. A hybrid scheme coordinating both the permissioned (Hyperledger) and public (Ethereum) blockchains is proposed to exploit their respective capabilities in terms of high transaction throughput and built-in cryptocurrency. A working prototype platform is implemented with a basic premium calculation model. The prototype system is deployed across Amazon Web Services (AWSs) cloud in a real-world Internet environment. A comprehensive performance study from the aspects of throughput, latency, and resource usage under different configurations is presented to show the solution's feasibility. The design practice and research findings are concluded in consort with the experience gained for further enhancing the proposed solution and extending the functional features such as a more realistic insurance policy to be applied in generic vehicle insurance applications.
Abstract We study the fundamental differences that separate: Litecoin; Bitcoin Gold; Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw some analysis to how these features are appreciated by the market, to ultimately make an inference as to how future successful cryptocurrencies may be invented and behave. We use Google Trend data, as well as price, volume and market capitalization data sourced from coinmarketcap.com to support this analysis. We find that Litecoinâs shorter block times offer benefits in commerce, but drawbacks in the mining process through orphaned blocks. Zcash holds a niche use for anonymous transactions, benefitting areas of the world lacking in economic freedom. Bitcoin Cash suffers from centralization in the mining process, while the greater decentralization of Bitcoin Gold has generally left it to stagnate. Etherâs greater functionality offers the greatest threat to Bitcoinâs dominance in the market. A coin that incorporates several of these features can be technically better than Bitcoin, but the first-to-market advantage of Bitcoin should keep its dominant position in the market.
Callbacks are an effective programming discipline for implementing event-driven programming, especially in environments like Ethereum which forbid shared global state and concurrency. Callbacks allow a callee to delegate the execution back to the caller. Though effective, they can lead to subtle mistakes principally in open environments where callbacks can be added in a new code. Indeed, several high profile bugs in smart contracts exploit callbacks. We present the first static technique ensuring modularity in the presence of callbacks and apply it to verify prominent smart contracts. Modularity ensures that external calls to other contracts cannot affect the behavior of the contract. Importantly, modularity is guaranteed without restricting programming. In general, checking modularity is undecidableâeven for programs without loops. This paper describes an effective technique for soundly ensuring modularity harnessing SMT solvers. The main idea is to define a constructive version of modularity using commutativity and projection operations on program segments. We believe that this approach is also accessible to programmers, since counterexamples to modularity can be generated automatically by the SMT solvers, allowing programmers to understand and fix the error. We implemented our approach in order to demonstrate the precision of the modularity analysis and applied it to real smart contracts, including a subset of the 150 most active contracts in Ethereum. Our implementation decompiles bytecode programs into an intermediate representation and then implements the modularity checking using SMT queries. Overall, we argue that our experimental results indicate that the method can be applied to many realistic contracts, and that it is able to prove modularity where other methods fail.
Static analysis of smart contracts as-deployed on the Ethereum blockchain has received much recent attention. However, high-precision analyses currently face significant challenges when dealing with the Ethereum VM (EVM) execution model. A major such challenge is the modeling of low-level, transient âmemoryâ (as opposed to persistent, on-blockchain âstorageâ) that smart contracts employ. Statically understanding the usage patterns of memory is non-trivial, due to the dynamic allocation nature of in-memory buffers. We offer an analysis that models EVM memory, recovering high-level concepts (e.g., arrays, buffers, call arguments) via deep modeling of the flow of values. Our analysis opens the door to Ethereum static analyses with drastically increased precision. One such analysis detects the extraction of ERC20 tokens by unauthorized users. For another practical vulnerability (redundant calls, possibly used as an attack vector), our memory modeling yields analysis precision of 89%, compared to 16% for a state-of-the-art tool without precise memory modeling. Additionally, precise memory modeling enables the static computation of a contractâs gas cost. This gas-cost analysis has recently been instrumental in the evaluation of the impact of the EIP-1884 repricing (in terms of gas costs) of EVM operations, leading to a reward and significant publicity from the Ethereum Foundation.
Ilhaam A. Omar, Mazin Debe, Raja Jayaraman, Khaled Salah ¡ 6 authors
<div>The COVID-19 pandemic has severely impacted many industries, in particular the healthcare sector exposing systemic vulnerabilities in emergency preparedness, risk mitigation, and supply chain management. A major challenge during the pandemic was related to the increased demand of Personal Protective Equipment (PPE) resulting in critical shortages for healthcare and frontline workers. The lack of information visibility combined with the inability to precisely track product movement within the supply chain requires an robust traceability solution. Blockchain technology is a distributed ledger that ensures a transparent,</div><div>safe, and secure exchange of data among supply chain stakeholders. The advantages of adopting blockchain technology to manage and track PPE products in the supply chain include decentralized control, security, traceability,</div><div>and auditable time-stamped transactions. In this paper, we present a blockchain-based approach using smart contracts to transform PPE supply chain operations. We propose a generic framework using Ethereum smart contracts and</div><div>decentralized storage systems to automate the processes and information exchange and present detailed algorithms that capture the interactions among supply chain stakeholders. The smart contract code was developed and tested in Remix environment, and the code is made publicly available on Github. We present detailed cost and security analysis incurred by the stakeholders in the supply chain. Adopting a blockchain-based solution for PPE supply chains is economically viable and provides a streamlined, secure, trusted, and transparent mode of communication among various stakeholders.</div>
ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this article, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design.
Sarthak Goyal, K Sanjith, Ambali Sisodia, N M Suhaas ¡ 5 authors
Blockchain technology has lately gained notability as a way of creating diffused trust-free systems, where economic transactions are guaranteed by the underlying blockchain. Transaction is how the extraneous world interconnect with the Ethereum network. Transactions are used when we have an inclination to amend or apprise the state stored in the Ethereum network. In this paper we have explained the role played by transactions in the Ethereum network, how they can be implemented by execution of certain programs and how these programs are executed are important aspects to gain a deep understanding of Ethereum blockchain network. Blockchain technology requires an in depth understanding of its advantages, disadvantages and how it can be employed in various fields of technology. Innovative solutions that go beyond crypto-currencies such as Bitcoin may have the potential for fundamentally changing society.
The traditional covert communication that relies on a central node is vulnerable to detection and attack. Applying blockchain to covert communication can improve the channel's anti-interference and antitampering. Whisper is the communication protocol of Ethereum, which mainly relies on payload to store information and padding to expand. These two fields can store a large amount of information, creating conditions for the realization of covert communication. In this paper, we propose a covert communication method based on the whisper protocol to covertly transfer information in the blockchain. To implement this method, we use payload to store the carrier information, matching it with the secret message. The generated index is recorded in the padding field. To improve the concealment of communication, we simulate the default filling rules of the protocol to maintain the message size. A new topicâkey pair interaction method is also proposed to improve the security of the model. Moreover, the anti-interference, antitampering and antidetection of the newly proposed model are verified through theoretical analysis and experiment. The experimental findings show that the amount of information in the proposed method is 4.7 times that of the traditional time-based covert communication. The time consumption of information transmission is reduced to 52.25% under the same settings and even less in actual use. The cost of the new topicâkey pair interaction is reduced by nearly 50% compared with the original method.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
P. Sheela Rani, Sankara Revathi S, J S Dharshini, M Rekha
The Internet of Things (IOT) is integrated with supply chain management process to track the product. To track the product smart tags is used. The smart tags like QR code and NFC is used. But with the technology enhancement the block chain is introduced into the supply chain management process. The block chain is the great revolution that data in the centralized form is transformed in to a decentralized manner. The distributed Ledger Technology (DLT) is one of the method used in ethereum block chain. The main advantage of using DLT is, it offers decentralized, privacy-preserving and verifiable process in the smart tags. In existing system only single server was used to maintain all the process like supplier, manufacturer and distributor. In this application we are using different server which was more secure than existing system. The proposed solution in this paper is it checks the product evidence during the entire lifecycle of the product by using the smart contract. The data can be immutable by using smart contract with ethereum block chain. The duplication is manipulated by the block chainserver.
Abstract Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin-Ethereum and Bitcoin-Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.
Olamide Jogunola, Mohammad Hammoudeh, Kelvin Anoh, Bamidele Adebisi
The increasing integration of prosumers and smart metering devices into the energy distribution network, is transforming the traditional energy market to a community energy trading that requires peer-to-peer (P2P) interactions. Such P2P interactions result in complex data exchanges among prosumers, utility grid, and market operators. This inevitably introduces control complexity, security, and privacy challenges in the existing power system. The application of distributed ledger technology (DLT) has seen an increase in solving security challenges in the power network, specifically, in P2P energy exchanges. Thus, this study explores different DLT structures including blockchain and IOTA usage in energy P2P trading. A smart contract for managing trust and transactions is designed and implemented on IBM hyperledger fabric platform. In addition, we evaluated the performance of interconnected internet of things devices for energy transactions with IOTA protocol, which uses the directed acyclic graph as its DLT structure, against the Ethereum-based blockchain structure. It is shown that the end-to-end transaction delay with the IOTA DLT is lower than the Ethereum-based DLT implementation.
Smart contracts are computer programs allowing users to define and execute transactions automatically on top of the blockchain platform. Many of such smart contracts can be viewed as games. A game-like contract accepts inputs from multiple participants, and upon ending, automatically derives an outcome while distributing assets according to some predefined rules. Without clear understanding of the game rules, participants may suffer from fraudulent advertisements and financial losses. In this paper, we present a framework to perform (semi-)automated verification of smart contract fairness, whose results can be used to refute false claims with concrete examples or certify contract implementations with respect to desired fairness properties. We implement FairCon, which is able to check fairness properties including truthfulness, efficiency, optimality, and collusion-freeness for Ethereum smart contracts. We evaluate FairCon on a set of real-world benchmarks and the experiment result indicates that FairCon is effective in detecting property violations and able to prove fairness for common types of contracts.
One of the backbone technology of Blockchains is cryptography. In simpler terms cryptography is the mathematical art of secret writing, however it's applications are not limited to writing secret codes only. Essentially cryptography is used to achieve three main security goals namely confidentiality (in-formation must be hidden from the unintended users), integrity (information must be prevented from illicit modifications) and availability (information must be readily available to intended users at all times). In cryptography confidentiality is achieved by employing a technique called as Encryption, integrity is achieved by employing special one way functions called as Cryptographic Hash Functions and authentication (process for verifying identity of the sender) is achieved by employing a technique called as Digital Signatures. For better understanding about the working of Blockchain it is essential to understand cryptographic concepts on which Blockchain is built. This paper presents the brief overview of such cryptographic concepts. In particular this papers throws light on the cryptographic concepts used to build Bitcoin and Ethereum Blockchains. Furthermore this paper also throws light on the need and requirements of post-quantum cryptographic primitives for post-quantum Blockchains.
Natalia Trojanowska, MichaĹ KÄdziora, Moataz Hanif, Houbing Song
The purpose of this paper is to present Ethereum decentralized application development methodology with focus on security issues and its verification. We introduce key concepts that are related to developing decentralized applications and Crypto Collectibles games. Moreover, the requirements for blockchain projects were presented along with a selection of use case examples. The paper concerns the application design process issues, starting from the methodology used, going through the description of requirements and specification, ending up with the implementation. Finally, an overview of the issues associated with the security of Ethereum decentralized applications is presented. We compared guidelines from Ethereum Smart Contract Best Practices by ConsenSys, Smart Contract Security Verification Standard created by SecuRing, Decentralized Application Security Project introduced by NCC Group, Security Considerations from Solidity documentation, Ethereum Smart Contracts Security Recommendations from Guylando Knowledge Lists, and Smart Contract Weakness Classification and Test Cases. It was discussed which guideline should be followed and when should the verification take place, considering the life cycle of the application. The paper covers different security risks related to blockchain games along with examples of how vulnerabilities can arise, how they can be detected during security verification phase, and countermeasures to address them.