Ethereum is an open-source, decentralized applications to support a cryptocurrency-based trading platform. The recent Ethereum hard fork, called âIstanbulâ took place in October 2019 due to threats from multitude of competitors has been completed. This hard fork brings six Ethereum Improvement Proposals (EIPs) which includes code execution, blockchainâs mining algorithm, increase data storage process capacity and also reduce Gas costs. Although these changes intended to improve the performance of Ethereum blockchain, many people have raise concern that forking is controversial. Cryptocurrency forks usually brings big changes ahead and may have serious implication on the price of Ethereum. Many resource appropriators (coin holders) are anxious about when will be the next network update for Ethereum. Most of the rules and decisions are made before these participants join the system or leave in the hands of developers of the project. There is lack of effective means for most resource appropriators or stakeholders to participate in system of decision-making for the next network update. This article provides a comprehensive overview of how decision-making for network update to be made in Ethereum and to propose the implementation of the management rights concerning the design, implementation of future changes to the Ethereum platform in order to achieve a clear collective-choice arrangement that allow most resource appropriators and stakeholders to participate in the decision-making process within the Ethereum community.
Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale residual module is based on one-dimensional convolution, which is not only capable of adaptive detecting features of different time scales in multivariate time series, but also enables the fusion of these features. LSTM has the ability to learn long-term dependencies in series, which is widely used in financial time series forecasting. By mixing these two methods, the model is able to obtain highly expressive features and efficiently learn trends and interactions of multivariate time series. In the study, the impact of external factors such as macroeconomic variables and investor attention on the Bitcoin price is considered in addition to the trading information of the Bitcoin market. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures. Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRC-LSTM in short-term forecasting for multivariate time series of cryptocurrencies.
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 Accountable Distributed Learning (PA-DL), a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protection of the agents data, and Ethereum smart contracts to ensure accountability.
Smart contract execution engines are a central part of transaction processing in blockchains. One of the most widely used execution engines is the Ethereum Virtual Machine (EVM). EVM performance is a key determinant of the overall blockchain system. Being able to gather detailed insights into EVM performance characteristics is essential not only when implementing EVMs, but also when designing and dimensioning blockchain-based information systems. Today, there is no precise and easy-to-use solution to gather such information. To address this issue, we introduce EVM-Perf, a high-precision EVM evaluation, and analysis framework. Our framework allows detailed performance analysis of arbitrary EVM implementations. Unlike previous work, it leverages statical analysis methods to achieve higher levels of accuracy. We provide an open-source implementation of EVM-Perf and use it to conduct extensive performance analysis for different physical machines. It comprises both machines with x86_64 as well as ARMv8 instruction set architecture. We discuss how EVM-Perf supports system architects with machine selection as well as configurations of Ethereum deployments, e.g., determining Gas limits or block intervals.
Bitcoin, one of the major cryptocurrencies, presents great opportunities and\nchallenges with its tremendous potential returns accompanying high risks. The\nhigh volatility of Bitcoin and the complex factors affecting them make the\nstudy of effective price forecasting methods of great practical importance to\nfinancial investors and researchers worldwide. In this paper, we propose a\nnovel approach called MRC-LSTM, which combines a Multi-scale Residual\nConvolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to\nimplement Bitcoin closing price prediction. Specifically, the Multi-scale\nresidual module is based on one-dimensional convolution, which is not only\ncapable of adaptive detecting features of different time scales in multivariate\ntime series, but also enables the fusion of these features. LSTM has the\nability to learn long-term dependencies in series, which is widely used in\nfinancial time series forecasting. By mixing these two methods, the model is\nable to obtain highly expressive features and efficiently learn trends and\ninteractions of multivariate time series. In the study, the impact of external\nfactors such as macroeconomic variables and investor attention on the Bitcoin\nprice is considered in addition to the trading information of the Bitcoin\nmarket. We performed experiments to predict the daily closing price of Bitcoin\n(USD), and the experimental results show that MRC-LSTM significantly\noutperforms a variety of other network structures. Furthermore, we conduct\nadditional experiments on two other cryptocurrencies, Ethereum and Litecoin, to\nfurther confirm the effectiveness of the MRC-LSTM in short-term forecasting for\nmultivariate time series of cryptocurrencies.\n
Enclave applications that rely on blockchains for integrity and availability are vulnerable to eclipse attacks. In this paper, we present an approach for reliably detecting extended eclipse attacks, even when the adversary controls all network connectivity. By monitoring changes to the difficulty parameter in Proof-of-Work (PoW) protocols, our algorithm detects suppression of new blocks, as well as difficulty-lowering attacks that attempt to force an enclave client onto a malicious fork mined solely by an attacker. We present analysis that attackers have negligible probability of evading our block monitoring algorithm, and demonstrate its robustness to most historical fluctuations in difficulty on the Ethereum blockchain, resulting in a very low false-positive rate.
James Meijers, Guntur Dharma Putra, Grammateia Kotsialou, Salil S. Kanhere ¡ 5 authors
Billions of Internet of Things (IoT) devices deployed today collect massive amounts of potentially valuable data. To efficiently utilize this data, markets must be developed where data can be traded in real time. Blockchain technology offers a potential platform for these types of markets. However, previous proposals using blockchain technology either require trusted third parties such as data brokers, or necessitate a large number of on-chain transactions to operate, incurring excessive overhead costs. This paper proposes a trustless data trading system that minimizes both the risk of fraud and the number of transactions performed on chain. In this system, data producers and consumers come to binding agreements while trading data off chain and they only settle on chain when a deposit or withdrawal of funds is required. A credit mechanism is also developed to further reduce the incurred fees. Additionally, the proposed marketplace is benchmarked on a private Ethereum network running on a lab-scale testbed and the proposed credit system is simulated so to analyze its risks and benefits.
Public Blockchains (BC) in support of Smart Contracts (SC), e.g., Ethereum, enable everyone to coordinate in a decentralized model to manage scarce and valuable resources,e.g., cryptocurrencies. Such BCs allow for the building of SCs that own resources and manage a set of permissions describing who is allowed to interact with these resources and what actions they can apply to them. However, Programming Languages (PL) and run-time systems used in current BCs lack a secure, flexible, and straightforward way to implement permissions within their SCs, leading to erroneous implementations that allow unauthorized access. The best-known incident related to a permission problem was the "Parity Hack", which led to the "loss" of tokens, valued at approximately 31 M USD.A better and secure SC access control concept provides an improved path to managing permissions. Thus, this paper presents a novel concept for handling permissions compatible with functional SC languages leveraging opaque and substructural data types to provide capability-based permission management. The opaque data types enforce that only designated functions can create permission carrying capabilities. Substructural data types prevent an unpermitted duplication of capabilities.
We propose BEAAS, a cloud service for Attribute-Based Access Control (ABAC) with security guarantee provided through the use of blockchain, specifically Ethereum. It enables user organizations to verify whether its access control data as well as access mediation decisions made by the cloud service were indeed done in an authorized manner. All the changes to the various ABAC components and access history are added to the Ethereum blockchain using efficiently written smart contracts in Solidity. We have developed a prototype system on the Rinkeby Ethereum test network and verified its functionality.
We evaluate and analyze the performance of a local electricity market for energy trading that we implemented on the Ethereum platform. The energy trading is based on a double auction with multiple sellers and multiple buyers; the matched price and volume are determined by a trade reduction mechanism. We benchmark the performance of Ethereum using a systematic blockchain performance evaluation method. Based on this, we propose and analyze an efficient market operation method. In particular, we relate the limits on the scalability and real-time performance of the market to the throughput and latency of the Ethereum platform. We also identify the minimum resources necessary to operate an Ethereum client.
Mario Felipe Munoz, Kaiwen Zhang, Aamir Shahzad, Mustapha Ouhimmou
When it comes to sustainability, many certification systems live and die by their capacity to track a volume from source to destination. This is because customers (and consequently, auditors) are increasingly concerned with where products are sourced ("Are these local materials?") and what is their environmental impact is ("How much CO2 did this generate?"). The Forest Sustainability Council (FSC) created various frameworks to meet these demands. The FSC is responsible for tracking and labelling all the wood volumes that its members use. Though widely respected, their certifications rely on trusted third parties and extensive record keeping. Thus, they do not efficiently address problems like bribery and "creative" inventory practices.Blockchain technology is an attractive solution because it increases traceability, auditability and access to information. However, blockchain systems have limitations. Notably, they are inefficient for storing large amounts of data. The standard workaround is to use a multiple-database approach where large files are kept off-chain, creating a trade-off between lower transaction fees and lower availability, among other factors.In contrast with these approaches, we propose LogLog: an entirely on-chain system for tracking wood volumes throughout a supply chain and enforcing FSC certification standards. LogLog is ERC-1155 compliant and meets the base requirements for a forestry supply chain.We created two reference implementations of our design using Solidity and deployed them over the Ethereum blockchain. Our evaluation uses realistic parameters and suggests our prototype is a viable alternative to current multiple-database systems in contexts where data integrity is a strict requirement.
The security of distributed applications backed by blockchain technology relies mainly on keeping the associated cryptographic keys (i.e. private keys) in well-protected storage. Since they are the unique proof of ownership of the underlying digital assets. If the keys are stolen or lost, there is no way to recover the assets. The cold wallet is a good candidate for basic use cases, but it has a substantial challenge for more complex applications as it does not scale. Warm and hot wallets are more convenient options for blockchain-based solutions that aim to transact in a cloud environment. In this work, we focus on Hardware Security Module (HSM) based wallet. The HSM is the de-facto standard device designed to manage high-value cryptographic keys and to protect them against hacks. In this demonstration, we present an HSM-based working prototype that secures the entire life cycle of Ethereum public and private keys.
Ethereum is one of the oldest examples of blockchain technology provides a system that converts centralized storage to distributed and records transactions by way of decentralized and not by a centralized system and can be verified by each node, therefore it is suitable for storing fingerprints from official diploma documents and transcripts that are published. Smart contract is needed for making contract transactions to Ethereum with programming code, so contracts such as diplomas and transcripts uploaded on the Ethereum blockchain can distribute and produce diploma validation and the authenticity of transcripts with transaction hash, consensus, and comply with ERC-721 token standardization. The results showed that a sample of 5 electronic documents in pdf format with a transaction speed of 1 second on each file that were published and secured with Ethereum blockchain technology can be easily verified for authenticity, the system proposed and developed by us takes in consideration invalid and failure cases by giving the necessary feedback to the user.
Cyril Naves Samuel, Severine Glock, François Verdier, Patricia Guitton-Ouhamou
Enterprises are realizing their distributed ledger use-cases with private blockchains as it is more secure, efficient, and reliable compared to the public networks. Due to this reason we evaluate the behavior and performance of Ethereum clients namely Geth, OpenEthereum (Parity), and Hyperledger Besu along with their Proof of Authority consensus algorithms from an enterprise perspective. We propose a testing methodology adapted to Microsoft Azure Cloud infrastructure overcoming the drawbacks of existing works. We analyze the behavior in each of the clients from different Ethereum focused parameters. We propose a novel load-balancer middleware specific to Ethereum and integrate in our test tool for uniform and reliable distribution of transactions. We analyze the bottlenecks and their root causes in each of the clients and report it to the community.
In cryptography, many different data structures are used to store and encrypt data. Merkle Tree is such a data structure that provides a way to prove data integrity and validity. It requires less memory or disk space than other data structures, resulting in simple and fast computation. This paper first explains the detailed principles and then introduces the application fields of Merkle Tree. Merkle Tree uses the hash value of the data block as leaf node tags and the encrypted hash of the child node tags as other node tags. As data structures evolve and innovate, Merkle Tree provides help and inspiration for the future new data structures. Furthermore, we investigate various applications using Merkle Tree technology, including Smart Grid, Bitcoin, Ethereum, Interplanetary File System, Image Authentication, Public Security Audit of Cloud Computing, SIMulator for Blockchain Applications, Hardware Memory Integrity, and Certificate Transparency. These researches show that Merkle Tree applies to a wide range of fields and has great potential. In brief, the paper aims to help researchers better understand the Merkle Tree features and various roles and effects in different environments, contributing to the development of cryptography.
Chaos-based Image/Signal Encryption
Cloud Data Security Solutions
Advanced Steganography and Watermarking Techniques
Anonymous Zether, proposed by BĂźnz, Agrawal, Zamani, and Boneh (FCâ20), is a private payment design whose wallets demand little bandwidth and need not remain online; this unique property makes it a compelling choice for resource-constrained devices. In this work, we describe an efficient construction of Anonymous Zether. Our protocol features proofs which grow only logarithmically in the size of the "anonymity sets" used, improving upon the linear growth attained by prior efforts. It also features competitive transaction sizes in practice (on the order of 3 kilobytes).Our central tool is a new family of extensions to Groth and Kohlweissâs one-out-of-many proofs (Eurocrypt 2015), which efficiently prove statements about many messages among a list of commitments. These extensions prove knowledge of a secret subset of a public list, and assert that the commitments in the subset satisfy certain properties (expressed as linear equations). Remarkably, our communication remains logarithmic; our computation increases only by a logarithmic multiplicative factor. This technique is likely to be of independent interest.We present an open-source, Ethereum-based implementation of our Anonymous Zether construction.
Various e-voting systems have been provided with the goal of increasing security and reducing costs for a long time. Ethereum Blockchain is a major breakthrough in the tech industry that provides a highly secure platform. As Ethereum, a decentralized platform which runs decentralized applications (DApps), was introduced in the market, the idea of secured voting system finally seemed possible. Organizations have shifted their focus on applications that run on blockchain platforms. It is highly likely that a simple voting method will not lead to a clear majority. There can be a number of ways to deal with this issue, including other potentially voting process, thus costing time and resources which is exactly opposite of the main concern of e-voting systems. Thus, in our paper, we introduce the vote-trading concept where in the absence of a clear majority, votes can be redistributed to other candidates and also this 'majority' factor can be determined by the organization as per their requirement as different organizations can have different winning percentage. We discuss the design and implementation for the Ethereum blockchain based decentralized preferential e-voting system using Solidity programming language where we present the idea of giving preference to candidates, rather than one vote per candidate.
Cryptocurrencies do not only allow for money transfers in the absence of a trusted third party but also enable the execution of distributed applications. Due to the rapid pace of development of cryptocurrencies, the foundations of such applications have not been rigorously studied. This is particularly problematic since in these applications, real money is at stake, and security breaches regularly cause severe financial losses.In this thesis, we present two systematic approaches to reliably verify the security of distributed blockchain applications based on formal foundations. To this end, we focus on the cryptocurrencies with the highest market capitalization, Bitcoin and Ethereum. In Ethereum, distributed applications are realized as smart contracts, reactive programs written in Ethereumâs expressive scripting language. In contrast, Bitcoin supports only a basic scripting language, and advanced applications are realized as peer-to-peer cryptographic protocols that resort to the execution of simple smart contracts in case of disputes among peers. As a result, the challenge in verifying distributed applications on the Ethereum blockchain lies in the study and abstraction of the semantics of Ethereumâs evolved scripting language, whereas Bitcoin, the study of distributed applications, requires a systematic analysis of the cryptographic protocols.In the thesis, we first formalize the formerly under-specified semantics of Ethereumâs native smart contract language EVM bytecode and implement the semantics in the proof assistant F*. In this context, we formally characterize relevant generic properties for smart contract security, which capture real-world attack scenarios.We then survey existing automated static analyzers for Ethereum smart contracts unveiling the weaknesses in the semantic foundations of these tools and the practical impact of these weaknesses on the analysis results. Based on these findings, we propose our own automatic static analysis tool for Ethereum smart contracts, which comes with a rigorous soundness proof while still showing competitive performance. In this course, we also propose a general framework for the modular and semantic-driven development of automatic static analyzers. Finally, we study the security of payment channel networks for Bitcoin. Payment channel networks are distributed protocols that allow for efficient and cheap payments between Bitcoin users and offer a promising solution to Bitcoinâs scalability problems. We unveil a security issue in Bitcoinâs existing payment channel network implementation and formally characterize the relevant security and privacy notions in this context. We further develop a cryptographic primitive for the construction of payment channel networks with formal security guarantees
N. Suganthi, Gokul Sridharan, E Shravanth, K. Veena
Abstract: Democratic rule is based on safe and open electoral systems that can preserve their authenticity. The conventional voting processes are hampered significantly by electoral fraud and security breaches as well as centralized management and inefficiencies in auditability and accessibility. The issues of electoral systems reduce public trust along with lowering the credibility of election results. This paper introduces a new three-tier blockchain-based e-voting system designed to enhance voter privacy alongside system scalability and end-to-end verifiability to restore trust in electoral processes. Voter verification at Layer 1 (Identity Verification) combines Decentralized Identity (DID) with Zero-Knowledge Proofs (ZKP) and multimodal biometric techniques involving fingerprint scanning, facial recognition technology, and voice analysis. The system enables only the participation of valid voters while also protecting their private data and fulfilling different user needs. The system's Layer 2 (Vote Casting & Secure Storage) employs a hybrid consensus algorithm combining Byzantine Fault Tolerance (BFT) and Delegated Proof-of-Stake (dPoS) to store votes securely while reducing energy consumption. The system leverages Triple-Blind Signatures to provide complete voter anonymity by decoupling voter identities from their votes as well as any accompanying metadata. Lattice-based post-quantum cryptography is employed to encrypt votes which are distributed across sharded blockchain subnets for enhanced performance without sacrificing fault tolerance. The system accumulates votes via Merkle roots and verifies them via zk-SNARKs in Layer 3 (Result Processing & Transparency) that allows public observation without compromising voter privacy. A Live Audit Dashboard provides voters with the capability to check their vote in real time which facilitates transparent and accountable voting processes. The suggested system attains a secure and open electronic voting process using sophisticated cryptographic protocols in a decentralized setup compliant with international requirements while enabling digital democratic participation.
This paper describes blockchain enabled Auction House and e-marketplace. Traditional Auction house and e-marketplaces charges commission from users as well as have control over users They can block any merchant on their own wish without permission of merchant. Currently present auction houses charges large commissions. In this paper weâre proposing a platform using blockchain where seller can enlist product without ÂŹinvolvement of any third party. Our platform provides choices to seller to enlist product in different ways for selling like Direct Sell, Open Auction and Blind Auction. Different buyers can buy different products by different selling options. After comparison of cost using blockchain with online auction marketplace such as eBay it is observed that selling using blockchain decentralized app is cheaper than online options. Each transaction is verified through the blockchain and is recorded to the decentralized ledger. No trusted third parties. The transactions on the blockchain are trackable and irreversible thus buyers and the seller cannot breach the contract. This is the application which remedies all the drawbacks mentioned above.
Giacomo Ibba, Giuseppe Antonio Pierro, Marco Di Francesco
Ethereum is one of the most popular platforms for exchanging cryptocurrencies as well as the most established for peer to peer programming and smart contracts publishing [3]. The versatility of the Solidity language allows developers to program general-purpose smart contracts. Among the various smart contracts, there may be some fraudulent ones, whose purpose is to steal Ether from the network participants. A notorious example of such cases are Ponzi schemes, i.e. a financial frauds that require investors to be repaid through the investments of others who have just entered the scheme. Within the Ethereum blockchain, several contracts have been identified as being Ponzi schemes. The paper proposes a machine learning model that uses textual classification techniques to recognize contracts emulating the behavior of a Ponzi scheme. Starting from a contracts dataset containing exclusively Ponzi schemes uploaded between 2016 and 2018, we built models able to properly classify Ponzi schemes contracts. We tested several models, some of which returned an overall accuracy of 99% on classification. The best model turned out to be the linear Support Vector Machine and the Multinomial Naive Bayes model, which provides the best results in terms of metrics evaluation.
John WickstrĂśm, Magnus Westerlund, GĂśran Pulkkis
The proliferation of IoT devices in society demands a renewed focus on securing the use and maintenance of such systems. IoT-based systems have a significant impact on society and therefore such systems must have guaranteed resilience. We introduce a solution as a protocol that utilizes smart contracts on the Ethereum blockchain to enforce a security model that helps maintain distributed IoT networks in a healthy condition throughout their lifecycle. Our protocol enforces secured deployment, communication, management, and maintenance for hardened IoT devices. We additionally propose a smart contract solution for creating and handling generic IoT tasks. This solution enables users to manage devices by utilizing the Ethereum infrastructure for authentication, authorization, and communication. This design allows the devices to function autonomously by interpreting tasks received through smart contract transactions, without any direct human to device interaction. The result is a protocol that provides mechanisms for authentication and authorization, task provisioning, and audit trails.
Xiao Fan Liu, Huanhuan Ren, Si-Hao Liu, Xian-Jian Jiang
Abstract The cryptocurrency economy provides a comprehensive digital trace of human economic behavior: almost all cryptocurrency usersâ activities are faithfully recorded in transactions on public blockchains. However, the user identifiers in the transaction records, i.e., blockchain addresses, are anonymous. That is, they cannot be associated with any real âoff-chainâ identify of actual users. Nonetheless, identifying the economic roles of the addresses from their past behaviors is still feasible. This paper analyzes Ethereum token transactions, characterizes key economic agentsâ behavior from their transaction patterns, and explores their identifiability through interpretable machine learning models. Specifically, six types of most active economic agents are considered, including centralized cryptocurrency exchanges, decentralized exchanges, cryptocurrency wallets, token issuers, airdrop services, and gaming services. Transaction patterns such as trading volume, transaction tempo, and structural properties of transaction networks are defined for individual blockchain addresses. The results showed that cryptocurrency exchanges and online wallets have signature behavior patterns and hence can be accurately distinguished from other agents. Token issuers, airdrop services, and gaming services can sometimes be confused. Moreover, transaction networksâ features provide the richest information in the economic agentâs identification.
Blockchain technology's (BT) Ethereum Smart Contracts allows programmable transactions that involve the transfer of monetary assets among peers on a BT network independent of a central authorizing agency. Ethereum Smart Contracts are programs that are deployed as decentralized applications, having the building blocks of the blockchain consensus protocol. This technology enables consumers to make agreements in a transparent and conflict-free environment. However, the security vulnerabilities within these smart contracts are a potential threat to the applications and their consumers and have shown in the past to cause huge financial losses. In this paper, we propose a framework that combines static and dynamic analysis to detect Denial of Service (DoS) vulnerability due to an unexpected revert in Ethereum Smart Contracts. Our framework, SmartScan, statically scans smart contracts under test (SCUTs) to identify patterns that are potentially vulnerable in these SCUTs and then uses dynamic analysis to precisely confirm their exploitability of the DoS-Unexpected Revert vulnerability, thus achieving increased performance and more precise results. We evaluated SmartScan on a set of 500 smart contracts collected from the Etherscan. Our approach shows an improvement in precision and recall when compared to available state of the art techniques.