Performance contracts used for servitized business models enable consideration of overall life-cycle costs rather than just production costs. However, practical implementation of performance contracts has been limited due to challenges with performance evaluation, accountability, and financial concepts. As a solution, this paper proposes the connection of the digital building twin with blockchain-based smart contracts to execute performance-based digital payments. First, we conceptualize a technical architecture to connect blockchain to digital building twins. The digital building twin stores and evaluates performance data in real-time while the blockchain ensures transparency and trusted execution of automatic performance evaluation and rewards through smart contracts. Next, we demonstrate the feasibility of both the concept and technical architecture by integrating the Ethereum blockchain with digital building models and sensors via the Siemens building twin platform. The resulting prototype is the first full-stack implementation of a performance-based smart contract in the built environment.
Smart contract is one of the core features of Ethereum and has inspired many blockchain descendants. Since its advent, the verification paradigm of smart contract has been improving toward high scalability. It shifts from the expensive on-chain verification to the orchestration of off-chain VM (virtual machine) execution and on-chain arbitration with the pinpoint protocol. The representative projects are TrueBit, Arbitrum, YODA, ACE, and Optimism. Inspired by visionaries in academia and industry, we consider the DNN computation to be promising but on the next level of complexity for the verification paradigm of smart contract. Unfortunately, even for the state-of-the-art verification paradigm, off-chain VM execution of DNN computation has an orders-of-magnitude slowdown compared to the native off-chain execution. To enable the native off-chain execution of verifiable DNN computation, we present Agatha system, which solves the significant challenges of misalignment and inconsistency: (1) Native DNN computation has a graph-based computation paradigm misaligned with previous VM-based execution and arbitration; (2) Native DNN computation may be inconsistent cross platforms which invalidates the verification paradigm. In response, we propose the graph-based pinpoint protocol (GPP) which enables the pinpoint protocol on computational graphs, and bridges the native off-chain execution and the contract arbitration. We also develop a technique named Cross-evaluator Consistent Execution (XCE), which guarantees cross-platform consistency and forms the correctness foundation of GPP. We showcase Agatha for the DNN computation of popular models (MobileNet, ResNet50 and VGG16) on Ethereum. Agatha achieves a negligible on-chain overhead, and an off-chain execution overhead of 3.0%, which represents an off-chain latency reduction of at least 602x compared to the state-of-the-art verification paradigm.
As the importance of vehicle data increases, it has become very important to safely store them. However, because onboard diagnostics scanners generally used to store vehicle data are IoT devices, security and capacity issues exist to store data safely and efficiently. To address this, we propose a system that stores vehicle data safely and efficiently using blockchain and IPFS. Users can access the system through DApp, an Ethereum-distributed application, and manage their vehicle data. Various experiments have been conducted to demonstrate the superior performance of this system, and the experimental results show its advantages in terms of data-processing speed and cost.
Tyler Kell, Haaroon Yousaf, Sarah Levin Allen, Sarah Meiklejohn · 5 authors
Pyramid schemes are investment scams in which top-level participants in a hierarchical network recruit and profit from an expanding base of defrauded newer participants. Pyramid schemes have existed for over a century, but there have been no in-depth studies of their dynamics and communities because of the opacity of participants' transactions. In this paper, we present an empirical study of Forsage, a pyramid scheme implemented as a smart contract and at its peak one of the largest consumers of resources in Ethereum. As a smart contract, Forsage makes its (byte)code and all of its transactions visible on the blockchain. We take advantage of this unprecedented transparency to gain insight into the mechanics, impact on participants, and evolution of Forsage. We quantify the (multi-million-dollar) gains of top-level participants as well as the losses of the vast majority (around 88%) of users. We analyze Forsage code both manually and using a purpose-built transaction simulator to uncover the complex mechanics of the scheme. Through complementary study of promotional videos and social media, we show how Forsage promoters have leveraged the unique features of smart contracts to lure users with false claims of trustworthiness and profitability, and how Forsage activity is concentrated within a small number of national communities.
For proof-of-work blockchains such as Ethereum, the mining power decentralization is an important discussion point in the community. Previous studies mostly focus on the aggregated power of the mining pools, neglecting the pool participants who are the source of the pools' power. In this paper, we present the first large-scale study of the pool participants in Ethereum's mining pools. Pool participants are not directly observable because they communicate with their pools via private channels. However, they leave "footprints" on chain as they use Ethereum accounts to anonymously receive rewards from mining pools. For this study, we combine several data sources to identify 62,358,646 pool reward transactions sent by 47 pools to their participants over Ethereum's entire near 5-year history. Our analyses about these transactions reveal interesting insights about three aspects of pool participants: the power decentralization at the participant level, their pool-switching behavior, and why they participate in pools. Our results provide a complementary and more balanced view about Ethereum's mining power decentralization at a deeper level.
Yuzhou Chen, Ignacio Segovia-DomÃnguez, Yulia R. Gel
There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block of learning and representation mechanisms. In turn, many recent results show that topological descriptors of the observed data, encoding information on the shape of the dataset in a topological space at different scales, that is, persistent homology of the data, may contain important complementary information, improving both performance and robustness of DL. As convergence of these two emerging ideas, we propose to enhance DL architectures with the most salient time-conditioned topological information of the data and introduce the concept of zigzag persistence into time-aware graph convolutional networks (GCNs). Zigzag persistence provides a systematic and mathematically rigorous framework to track the most important topological features of the observed data that tend to manifest themselves over time. To integrate the extracted time-conditioned topological descriptors into DL, we develop a new topological summary, zigzag persistence image, and derive its theoretical stability guarantees. We validate the new GCNs with a time-aware zigzag topological layer (Z-GCNETs), in application to traffic forecasting and Ethereum blockchain price prediction. Our results indicate that Z-GCNET outperforms 13 state-of-the-art methods on 4 time series datasets.
Stock markets have a centralized structure that has a number of intermediaries and operational trade policies contributing to high transaction times. Blockchain has the capability to optimize market transactions using automation with high security to create a peer-to-peer trading environment. It reduces operational risk by enabling transparency, certitude and interoperability in fragmented market systems to eliminate the need for third party regulators to a large extent. In this paper, a decentralized stock exchange system is implemented with Distributed Ledger Technology (DLT) on Ethereum for executing trades by separating concerns into three different smart contracts: buyer, seller and exchange. The self-enforcing smart contracts used are highly flexible and optimized for parallel operation due to functional abstraction. The multi-contract model is compared to a single contract model, which handles all three aspects within the same contract, by executing sample trading data from NASDAQ. Transaction fees for the miner at 161 Gwei is 27.96% lesser for the single-contract system and 98.75% lesser for the multi-contract system than the brokerage fees of traditional traders for the same transactions. Experimental results indicate that the separation of concern results in transaction costs being 98.26% lower and transaction time being 28.70% lower than having a single contract.
Payment channel networks (PCNs) are proposed to improve the cryptocurrency scalability by settling off-chain transactions. However, PCN introduces an undesirable assumption that a channel participant must stay online and be synchronized with the blockchain to defend against frauds. To alleviate this issue, watchtowers have been introduced, such that a hiring party can employ a watchtower to monitor the channel for fraud. However, a watchtower might profit from colluding with a cheating counterparty and fail to perform this job. Existing solutions either focus on heavy cryptographic techniques or require a large collateral. In this work, we leverage smart contracts through economic approaches to counter collusions for watchtowers in PCNs. This brings distrust between the watchtower and the counterparty, so that rational parties do not collude or cheat. We provide detailed analyses on the contracts and rigorously prove that the contracts are effective to counter collusions with minimal on-chain operations. In particular, a watchtower only needs to lock a small collateral, which incentivizes participation of watchtowers and users. We also provide an implementation of the contracts in Solidity and execute them on Ethereum to demonstrate the scalability and efficiency of the contracts.
Algan Tezel, Pedro Febrero, Eleni Papadonikolaki, Ä°brahim Yitmen
The interest in the implementation of distributed ledger technologies (DLTs) is on the rise in the construction sector. One specific type of DLT that has recently attracted much attention is blockchain. Blockchain has been mostly discussed conceptually for construction to date. This study presents some empirical discussions on supply chain management (SCM) applications of blockchain for construction by collecting feedback for three blockchain-based models: project bank accounts (PBAs) for payments, reverse auction–based tendering for bidding, and asset tokenization for project financing. The feedback was collected from three focus groups and a workshop. The working prototypes for the models were developed on Ethereum. The implementation of blockchain in payment arrangements was found to be simpler than in tendering and project tokenization workflows. However, the blockchain integration of those workflows may have large-scale impacts on the sector in the future. A broad set of general and model-specific benefits/opportunities and requirements/challenges was also identified for blockchain in construction. Some of these include streamlined, transparent transactions and rational trust building, and the need for challenging the sector culture, upscaling the legacy information technology (IT) systems, and compliance with the regulatory structures.
Millions of smart contracts have been deployed onto Ethereum for providing various services, whose functions can be invoked. For this purpose, the caller needs to know the function signature of a callee, which includes its function id and parameter types. Such signatures are critical to many applications focusing on smart contracts, e.g., reverse engineering, fuzzing, attack detection, and profiling. Unfortunately, it is challenging to recover the function signatures from contract bytecode, since neither debug information nor type information is present in the bytecode. To address this issue, prior approaches rely on source code, or a collection of known signatures from incomplete databases or incomplete heuristic rules, which, however, are far from adequate and cannot cope with the rapid growth of new contracts. In this paper, we propose a novel solution that leverages how functions are handled by Ethereum virtual machine (EVM) to automatically recover function signatures. In particular, we exploit how smart contracts determine the functions to be invoked to locate and extract function ids, and propose a new approach named type-aware symbolic execution (TASE) that utilizes the semantics of EVM operations on parameters to identify the number and the types of parameters. Moreover, we develop SigRec, a new tool for recovering function signatures from contract bytecode without the need of source code and function signature databases. The extensive experimental results show that SigRec outperforms all existing tools, achieving an unprecedented 98.7 percent accuracy within 0.074 seconds. We further demonstrate that the recovered function signatures are useful in attack detection, fuzzing and reverse engineering of EVM bytecode.
Youssef Faqir-Rhazoui, Miller-Janny Ariza-Garzón, Javier Arroyo, Samer Hassan
Blockchain technology has enabled a thriving emergent ecosystem of tools and communities actively using decentralized systems. However, most blockchain infrastructure (e.g. Ethereum) requires users to pay some fees to execute their desired actions in these novel online services. To which extent an increase in the price of such fees negatively affects user activity? Would significant price surges deter users from using blockchain-enabled online services? In this work, we study the 2020 surge of transaction fee price in the Ethereum network, and analyze how that affected user activities. Our use cases are the blockchain-enabled Decentralized Autonomous Organizations (DAOs) from the platforms DAOstack and DAOhaus. Thus, we analyzed 5,580 transactions from 7,825 users grouped in 191 DAO communities, using a VAR model with a daily time series of the average fee value and the DAO operations. Our results show just a minor influence of the fee (gas) price and the activity of DAO users. The insensitivity of the activity to the fee price is an anomaly in a supposedly self-regulated market, and we consider this should be tackled in future implementations.
The peer-to-peer (P2P) network of blockchain used to transport its transactions and blocks has a high impact on the efficiency and security of the system. The P2P network topologies of popular blockchains such as Bitcoin and Ethereum, therefore, deserve our highest attention. The current Ethereum blockchain explorers (e.g., Etherscan) focus on the tracking of block and transaction records but omit the characterization of the underlying P2P network. This work presents the Ethereum Network Analyzer (Ethna), a tool that probes and analyzes the P2P network of the Ethereum blockchain. Unlike Bitcoin that adopts an unstructured P2P network, Ethereum relies on the Kademlia DHT to manage its P2P network. Therefore, the existing analytical methods for Bitcoin-like P2P networks are not applicable to Ethereum. Ethna implements a novel method that accurately measures the degrees of Ethereum nodes. Furthermore, it incorporates an algorithm that derives the latency metrics of message propagation in the Ethereum P2P network. We ran Ethna on the Ethereum Mainnet and conducted extensive experiments to analyze the topological features of its P2P network. Our analysis shows that the Ethereum P2P network possesses a certain effect of small-world networks, and the degrees of nodes follow a power-law distribution that characterizes scale-free networks.
EIP-1559 is a new proposed pricing mechanism for the Ethereum protocol developed to bring stability to fluctuating gas prices. To properly understand this as a stochastic process, it is necessary to develop the mathematical foundations to understand under what conditions the base fee gas price outcomes behave as a stationary process, and when it does not. Understanding these mathematical fundamentals is critical to properly engineering a stable system.
The second generation of blockchains introduces the notion of "smart contract" to decentralized ledgers, but with each new blockchain system comes di erent consensus mechanisms or di erent approaches on how to assess the cost of computation inside the chain, both aspects that a ect the e ciency of the systems as a decentralized computer. We present an experimental comparison of two blockchain systems, namely Ethereum and Tezos, from the perspective of smart contracts, centered around the same implementation of a VCG for Sponsored Search auction algorithm, respectively encoded in Solidity and SmartPy. Our analysis shows the feasibility of implementing an algorithm for sponsored search in such an environment while providing information on how useful these systems can be for this type of smart contracts.
Blockchain technology (BT) Ethereum Smart Contracts allows programmable\ntransactions that involve the transfer of monetary assets among peers on a BT\nnetwork independent of a central authorizing agency. Ethereum Smart Contracts\nare programs that are deployed as decentralized applications, having the\nbuilding blocks of the blockchain consensus protocol. This technology enables\nconsumers to make agreements in a transparent and conflict-free environment.\nHowever, the security vulnerabilities within these smart contracts are a\npotential threat to the applications and their consumers and have shown in the\npast to cause huge financial losses. In this paper, we propose a framework that\ncombines static and dynamic analysis to detect Denial of Service (DoS)\nvulnerability due to an unexpected revert in Ethereum Smart Contracts. Our\nframework, SmartScan, statically scans smart contracts under test (SCUTs) to\nidentify patterns that are potentially vulnerable in these SCUTs and then uses\ndynamic analysis to precisely confirm their exploitability of the\nDoS-Unexpected Revert vulnerability, thus achieving increased performance and\nmore precise results. We evaluated SmartScan on a set of 500 smart contracts\ncollected from the Etherscan. Our approach shows an improvement in precision\nand recall when compared to available state-of-the-art techniques.\n
As the internet is becoming more accessible and convenient, larger numbers of people and businesses are shifting towards digital transactions. Digital payment methods are quicker, cheaper and much more efficient. Therefore, it's not surprising that newer forms of digital payment systems are rapidly being developed. When compared no other method comes even close to the giant that is cryptocurrency. Cryptocurrencies like Bitcoin and Ethereum are among the most popular forms of digital payments. Cryptocurrencies could be popularized in India as a viable option for digital currency, but it has both pros and cons which need to be acknowledged. Through the means of literature review, this research paper will analyze the cryptocurrency, its working and perform a comparative study between China and India. Alos, it considers the current status as well as the scope of cryptocurrency in India.
There are mainly three traditional data sharing methods. The first is the most direct data copy, the second is to share data based on a data sharing protocol, and the third is to share data through a data center. These methods have a common feature, that is, the data requester will get the data of the data owner. This may cause serious problems in data security, such as data leakage and data abuse. As a data center is a centralized organization, there are risks such as data loss and data tampering. In addition, various countries have also issued a series of policies on data security issues, such as the GDPR implemented by the European Union in 2018. The blockchain technology using a decentralized model can be used as a new attempt to solve the above problems. This paper studies a data sharing scheme based on blockchain, and proposes a model that combines the Ethereum blockchain and federated learning ideas, and uses off-chain storage methods to share data. In this model, users can upload data description information to the blockchain through smart contracts, and can retrieve the required data through keywords, and then send the data identification and data processing model to the data owner in the form of transactions. The data owner can use this model to process the data, and finally return the result to the data requester. Because the data owner is in full control of his data and does not expose the source data to the outside, the use of this model for data sharing can effectively avoid problems such as data leakage, data loss, and data abuse.
Xianxian Li, Jie Li, Feng Yu, Xuemei Fu · 6 authors
Encrypted image retrieval may return incorrect or incomplete results due to threats from malicious cloud servers. Most of the existing solutions focus on the efficiency and accuracy of retrieval, lack of verification of the completeness of search results, to achieve the reliability of search results and the transparency of the search process, we explore characteristic such as the decentralization and tamper-proof of blockchain, proposed a blockchain-based encrypted image retrieval scheme. This scheme stores the encrypted index on the blockchain (Ethereum), through the blockchain consensus mechanism and the function of searching on the smart contract, ensures the integrity and correctness of search results, then outsources the corresponding encrypted images to the cloud server to reduce storage cost, and designs a double-layer index structure using the bag of visual word model and simhash in the process of image similarity index. Experiments show that the reliability, high retrieval efficiency, and precision of the scheme also have a good privacy protection effect.
Advanced Steganography and Watermarking Techniques
E Chen, Bohan Qin, Yan Zhu, Weijing Song · 7 authors
In recent years, advanced smart contract languages (ASCLs) have been proposed to solve the problem of difficult reading, comprehension, and collaboration when writing smart legal contracts among people in different fields. However, this kind of languages are still hard to put into practice due to the lack of an effective conversion method from the ASCLs to executable smart contract programs. Aiming at this problem, we take SPESC as example to explore how to design conversion rules from the contract in it to the target programming language in Solidity, and to propose a three-layer smart contract framework, including advanced smart-contract layer, general smart-contract layer, and executable machine-code layer. These rules provide an approach to convert the definition of SPESC contracting parties into party-contracts on target language, as well as to produce SPESC contract terms into main-contract on target language. Moreover, the proposed framework specifies not only program architecture and storage structure on general smart-contract layer, but also important mechanisms, including personnel management, timing control, exception handling, etc., which can assist programmers to write smart contract programs. Furthermore, taking four SPESC contracts as testing objects, we provide the whole process of converting from SPESC contracts to Solidity programs by the SPESC-Translator, and verify the efficiency and security of the conversion process, including coding, deploying, running, and testing through Ethereum. The instance results show that the conversion rules and the three-layer framework can simplify the writing of smart contracts, standardize the program structure, and help programmers to verify the correctness of the contract programs.