Cryptocurrency forensics have become standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, this implicitness complicates investigations. On the other hand, it can have far-reaching consequences for the rights of those affected. Argumentation schemes could remedy this untenable situation by rendering the underlying premises more transparent. Additionally, they can aid in critically evaluating the probative value of any results obtained by cryptocurrency deanonymisation techniques. In the argumentation theory and AI community, argumentation schemes are influential as they state the implicit premises for different types of arguments. Through their critical questions, they aid the argumentation participants in critically evaluating arguments. We specialise the notion of argumentation schemes to legal reasoning about cryptocurrency deanonymisation. Furthermore, we demonstrate the applicability of the resulting schemes through an exemplary real-world case. Ultimately, we envision that using our schemes in legal practice can solidify the evidential value of blockchain investigations, as well as uncover and help to address uncertainty in the underlying premises—thus contributing to protecting the rights of those affected by cryptocurrency forensics.
In the wake of blockchain and Web3 technological advancements, Non-Fungible Tokens (NFTs) have emerged as prominent digital assets within the realms of art, gaming, and virtual commodities. Unlike their traditional counterparts, NFT auctions harness the virtues of decentralization, transparency, and immutability, ushering in a new era for trading artworks and other digital assets. This paper embarks on an exploration, first laying down the foundational principles of blockchain technology and NFTs. A comparative analysis follows, juxtaposing the dynamics of NFT auctions with the modus operandi of traditional auctions. Within the theoretical scaffold, the nuances of decentralization and trust in NFT auctions are elucidated, spotlighting the pivotal role of smart contracts throughout the auction trajectory. The emphasis also gravitates towards transparency and security, two cornerstones ensuring the integrity of the auction process. Diving into the methodology, this section delineates the research blueprint and the techniques employed for program testing. Delving into the practicalities, the discourse meticulously unpacks the architecture and operability of the smart contract, gauging its efficacy through rigorous assessments. Beyond the present scope, the paper ventures to uncover potential applications and horizons awaiting NFT auctions across diverse sectors.
Smart contracts are computer programs running on blockchains to implement Decentralized Applications. The absence of contract specifications hinders routine tasks, such as contract understanding and testing. In this work, we propose a specification mining approach to infer contract specifications from past transaction histories. Our approach derives high-level behavioral automata of function invocations, accompanied by program invariants statistically inferred from the transaction histories. We implemented our approach as tool SMCON and evaluated it on eleven well-studied Azure benchmark smart contracts and six popular real-world DApp smart contracts. The experiments show that SMCON mines reasonably accurate specifications that can be used to enhance symbolic analysis of smart contracts achieving higher code coverage and up to 56 % speedup, and facilitate DApp developers in maintaining high-quality documentation and test suites.
Lixing Chen, Feng Gao, Yang Bai, Jun Wu · 6 authors
Blockchain has revolutionized a variety of fields by providing decentralization, immutability, transparency, and auditability. This paper designs Blockchained Edge Resource Auction (BERA) for edge computing systems to allocate computing resources to application service providers (ASP) in a secure manner. BERA comprises two key components: Blockchain-based Sealed-Bid Auction (BSBA) and Graph Neural Network (GNN)-based Fraud Detection (GFD). BSBA designs smart contracts to realize sealed-bid auctions overhead blockchain. It incorporates the homomorphic commitment technique to guarantee the transactional privacy of ASPs’ bidding information and performs interval membership zero-knowledge proof to verify the legitimacy of auction results. While the privacy-preserving property of BSBA is desirable, the veiled bidding information tends to breed fraudulent behaviors. Therefore, GFD is further proposed to identify abnormal auction behaviors in BSBA without revealing bidding information of ASPs. GFD converts the blockchain data of BSBA to an auction behavioral graph of ASPs, and uses GNN to discover stealth frauds based on interactive patterns. In addition, we design a subgraph extraction scheme for GFD to improve its scalability. We implement BERA on a private Ethereum blockchain and successfully realize edge resource auctions. We simulate several types of auction frauds and identify them with GFD. The experimental results show that our method outperforms other benchmarks.
Ahmed Gouda Mohamed, Fahad Alqahtani, Mohamed Sherif, Sama Moustafa El-Shamie
The construction industry embodies a paramount role in the economic growth of various nations, including the Middle East and North Africa (MENA) region. Despite its significance, the industry faces construction disputes and payment issues, inducing financial losses and project postponements. Consequently, the construction industry has mutated smart contracts to enhance operational efficiency, which automates contract management tasks and offers perks, including transparency and efficiency. However, the adoption of smart contracts in the MENA construction sector remains limited, and the region lacks thorough research on this topic. This study addresses this gap by examining the level of cognizance and apprehension of smart contracts among construction industry stakeholders in the MENA region. The research employs Structural Equation Modeling (SEM) to analyze the relative importance of implementing smart contracts within different project lifecycle phases and relevant practices and identify the hindrances impacting MENA’s smart contract deployment. A practical implementation of smart contract-based tendering employing the Trakti platform is paraded. The findings unveiled the infancy of smart contract adoption in the MENA construction sector. They revealed the substantial importance of implementing smart contracts in the Project Execution Phase, Project Closure, and Monitoring and Control Phase, attaining relative weights of 23.23%, 21.62%, and 20.50%, respectively.
Web 3 is considered the next generation of the internet. Decentralized autonomous organizations (DAOs) are considered the next avatar of organizations run digitally over blockchain-led technology platforms. Business logic and rules for running the organization are programmed in distributed applications (dApps) and executed using smart contracts. Token-based rights allow owing members to vote, participate in governance, and direct how the organization will be run. While DAOs are facing several legal and regulatory challenges on one side and fighting with technical vulnerabilities and hacks on the other side, future research in this field appears promising. There is an enormous need for education and awareness of the functioning of these emerging models, which can be dealt with using a multi-faceted approach. Decentralized governance can have a massive societal impact and lead to an equitable world. It drives financial inclusion and puts automatic decision-making at the fore.
Smart contracts codify real-world transactions and automatically execute the terms of the contract when predefined conditions are met. This paper proposes SmartML, a modeling language for smart contracts that is platform independent and easy to comprehend. We detail its formal semantics and type system with a focus on its role in addressing security vulnerabilities. We show along a case study, how SmartML contributes to the prevention of reentrancy attacks, illustrating its efficacy in reinforcing the reliability and security of smart contracts within decentralized systems.
We consider Geometric Mean Market Makers (G3Ms) – a special type of Decentralized Exchange – with two types of traders: liquidity takers and arbitrageurs. Liquidity takers use G3Ms to swap tokens and to speculate, while arbitrageurs exploit arbitrage opportunities arising from misalignments between the G3M's price and the external market price. We show that in continuous time, a G3M charging proportional transaction fees offers exchange rates that are of finite variation, and that the opportunity cost of providing liquidity relative to rebalancing a self-financing constant-weights portfolio is, in fact, a non-negative gain. Moreover, we demonstrate that Impermanent Loss can be super-hedged in continuous time by a model-free rebalancing strategy. We conclude with a numerical analysis discussing the approximative nature of our continuous-time results for trading in discrete time.
The pivotal role of semiconductors in propelling the rapid advancement of technologies, such as artificial intelligence, electric vehicles, and robotics, has prompted global attention toward bolstering semiconductor competitiveness.A fabless firm's performance is a determinant factor in this landscape.However, the dominance of established global fabless firms in the semiconductor market presents formidable barriers to the ingress and expansion of new entrants.Consequently, this study advocates for a novel semiconductor business model utilizing smart contracts based on emerging blockchain technology.To substantiate the efficacy of this model, a survey encompassed 106 semiconductor experts across 11 nations.Results indicated that 75% of respondents perceived an enhancement in fabless growth of the semiconductor business model.They affirmed the potential for acquiring new clientele, thereby highlighting the significance of this business model in furnishing a novel growth trajectory for fabless semiconductor companies seeking entry into the global market.
Although online auctions have gained popularity as a marketplace format, they encounter challenges stemming from bidders withdrawing high bids without penalty, and wasting organizer resources due to redundant auction rounds. This behavior, known as overbidding, undermines efficiency and remains a longstanding issue in traditional auction designs. It’s worth noting that one of the common reasons for bid withdrawals is exceeding their financial capacity. Participants bid beyond their budgetary constraints, not necessarily with the goal of obtaining the asset but for alternative motives. In an effort to address these overbidding inefficiencies, this paper presents Zk-Auction, a decentralized blockchain-based cross-chain auction system. Zk-Auction leverages the privacy-preserving capabilities of zero-knowledge proofs to cryptographically verify bidder funds for each bid, deterring inflation beyond actual holdings. A novel sidechain architecture simulates interoperability between independent "Banking" and "Auction" blockchains, enabling bid authentication without revealing identities or balances. By tackling the core challenges of existing centralized auction platforms, Zk-Auction adopts a distributed off-chain matching and on-chain settlement approach to maintain security and interoperability across heterogeneous distributed ledgers. The integration of Zero-Knowledge Proofs in bid validation ensures a privacy-preserving manner. Additionally, a proof-of-concept prototype demonstrates feasibility at scale. Evaluations of the system show enhanced participant accountability, thereby incentivizing sincere bidding behavior in contrast to traditional designs that are susceptible to wasteful overbidding.
Xuan Liu, Lu Liu, Yong Yuan, Yonghong Long · 6 authors
Recent years have witnessed remarkable developments and increasingly deepened integrations between blockchain as a decentralized computing architecture and auction as an efficient resource allocation approach. Typically, blockchain can help provide a secured and trusted distributed environment for various auction scenarios, while auction is particularly suitable for designing resource allocation and pricing mechanisms in blockchain systems. As such, integrative research on blockchain and auction developed rapidly and attracted widespread attention in various fields ranging from academia to financial, industrial, and social services. However, a comprehensive survey on this interdisciplinary topic is still nonexistent, which motivates our work. In this article, we aim to fill this important research gap by reviewing the related literature. We first conducted a brief overview of blockchain technology and auction theory, and then systematically discussed the research progress on the existing blockchain research based on auction theory as well as auction research enabled by blockchain. Toward the end, we presented several open research issues and directions, aiming to provide useful guidance and reference for future research efforts.
Online data trading is increasingly prevalent as data are becoming valuable assets. In most common conventional data trading scenarios, three parties (seller, broker, and buyer) exist, and fairness in trading is essential. This paper discusses and solves the fairness problem in two aspects. First, we considerexchange fairness, which requires payments and data exchanged correctly between buyers and the broker. In existing solutions, keys of encrypted data are traded. However, these solutions failed to provide a complete and secure design for validating keys' correctness unless they used generic theoretical but expensive methods, e.g., zk-SNARK. We address this security issue by designing a new key verification mechanism. We also present a novel atomic exchange protocol based on Hashed Timelock Contracts on Ethereum, reducing gas consumption compared to the existing approach. Second, we considerdistribution fairness, which requires correctly splitting income between the broker and sellers. Straightforward solutions are impractical, i.e., sellers participating in every transaction or traversing the blockchain. Therefore, we design a verifiable statement protocol for sellers to verify the income split efficiently. Further, analysis and experimental results indicate that extra fairness properties are securely achieved, and our protocol reduces users' on-chain participation compared to state-of-the-art protocols.
Given the growing importance of smart contracts in various applications, ensuring their security and reliability is critical. Fuzzing, an effective vulnerability detection technique, has recently been widely applied to smart contracts. Despite numerous studies, a systematic investigation of smart contract fuzzing techniques remains lacking. In this paper, we fill this gap by: 1) providing a comprehensive review of current research in contract fuzzing, and 2) conducting an in-depth empirical study to evaluate state-of-the-art contract fuzzers' usability. To guarantee a fair evaluation, we employ a carefully-labeled benchmark and introduce a set of pragmatic performance metrics, evaluating fuzzers from five complementary perspectives. Based on our findings, we provide direction for the future research and development of contract fuzzers.
Sangtian Guan, Juanjuan Li, Wenwen Ding, Fei–Yue Wang
In response to concerns over the centralization tendency in the decentralized autonomous organizations (DAOs), TRUE autonomous organizations and operations (TAOs or TRUE DAOs) have been proposed recently. TAOs aim at spreading equitable value distribution and democratized decision-making, distinguishing them from their DAOs counterparts. This study focuses on the treasury within TAOs, which acts as a central fund pool and a crucial element in the decentralized economy (DeEco) system. First, against a backdrop of potential black swan events and other long-tail unforeseen challenges, a reference model for the intelligent treasury management of TAOs is proposed. Then, an evaluation system, namely VALID, is presented with metrics including verifiability, anti-volatility, legitimacy, inclusiveness, and decentralization. Furthermore, a novel parallel treasury management mechanism is proposed to demonstrate a virtual-real interactive closed-loop management and control paradigm of the treasury, thereby fostering the formulation and development of DeEco. This research provides a comprehensive perspective on intelligent treasury management of TAOs and their role in sustainable advancement of DeEco.
Hongyu Guo, Haozhe Liang, Ju Huang, Wei Ou · 7 authors
The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.
Yuzheng Ren, Renchao Xie, F. Richard Yu, Ran Zhang · 7 authors
“Read-write-own” based Web3 has been proposed as a promising user-centric Internet to open the new generation of the World Wide Web, where Web3 users can independently manage data and derive value from creating content without relying on intermediaries. Connected and autonomous vehicles (CAVs) in Web3 can trade models in a self-controlled and decentralized credible way, which is a fundamentally and principally innovation based on novel architecture. Effectively implementing such paradigms involves proper model trading strategies. However, reinforcement learning (RL)-based strategies face challenges of poor generalization ability, low feasibility, and the exploration-exploitation dilemma. It is also difficult to define an explicit and appropriate reward function. Therefore, in this paper, we propose an intelligence-based reinforcement learning (IRL) approach for CAVs in Web3. We present a framework to enable model transactions between CAVs. Also, we provide a decentralized identifier (DID)-based identity management system for resource description and data verification to access Web3, followed by the mechanism and supporting smart contracts. Furthermore, we formulate the model trading issue as an active inference to form higher-level cognition about the environment without rewards. Then we use IRL to solve it. And we use “intelligence”, a high-level indicator, to quantify the efficiency of such cognition. It can evaluate the difference between the predicted state and the real state in policy exploration. The proposed scheme shows good generalization and can auto-balance exploration and exploitation, simultaneously achieving outperforming performance on the model trading issue with no rewards. In simulations, the performance of the proposed scheme is compared with existing methods.
James A. Cunningham, Nigel Davies, Sarah Devaney, Søren Holm · 7 authors
Abstract Decentralized autonomous organizations (DAOs) have emerged as a novel governance mechanism that operates through distributed ledgers and smart contracts, enabling members to direct an organization's actions. The widespread adoption of DAOs has occurred in response to their utility in managing emergent semi‐structured projects and has led to the development of various innovative governance mechanisms. The mechanisms employed by DAOs has the potential to be generalized beyond their core financial domain to a wide range of use cases. In the medical field the use of blockchain and DAOs can provide secure and transparent access to medical data, while ensuring patient privacy. Civic access to medical data is a growing area of interest, where individuals have control over their own medical data and can share it with healthcare providers, researchers, and other stakeholders. DAOs can facilitate this civic access, enabling individuals to share their data securely and selectively with authorized parties for research and other purposes. This paper explores the use of DAOs to medical data sharing, with a focus on ownership, governance, and transaction models. An application framework and API that enables the deployment of DAO‐like organizations is derived and this approach is applied to the patient‐centric management of medical data.
Burak Öz, Jonas Gebele, Parshant Singh, Filip Rezabek · 5 authors
Maximal Extractable Value (MEV) searching has gained prominence on the Ethereum blockchain since the surge in Decentralized Finance activities. In Ethereum, MEV extraction primarily hinges on fee payments to block proposers. However, in First-Come-First-Served (FCFS) blockchain networks, the focus shifts to latency optimizations, akin to High-Frequency Trading in Traditional Finance. This paper illustrates the dynamics of the MEV extraction game in an FCFS network, specifically Algorand. We introduce an arbitrage detection algorithm tailored to the unique time constraints of FCFS networks and assess its effectiveness. Additionally, our experiments investigate potential optimizations in Algorand's network layer to secure optimal execution positions. Our analysis reveals that while the states of relevant trading pools are updated approximately every six blocks on median, pursuing MEV at the block state level is not viable on Algorand, as arbitrage opportunities are typically executed within the blocks they appear. Our algorithm's performance under varying time constraints underscores the importance of timing in arbitrage discovery. Furthermore, our network-level experiments identify critical transaction prioritization strategies for Algorand's FCFS network. Key among these is reducing latency in connections with relays that are well-connected to high-staked proposers.
Mathew Fukuzawa, Brandon M. McConnell, Michael G. Kay, Kristin Thoney-Barletta · 5 authors
Purpose Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts. Design/methodology/approach Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results. Findings The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally. Research limitations/implications Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience. Practical implications Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost. Social implications Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams. Originality/value This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.
The development of low-carbon power systems has not only elevated the investment costs of power enterprises, but also generated a vast amount of electricity data. The electricity data trading holds promising potential as a primary means to cover investment costs. However, there is a lack of research on the electricity data trading. To address this issue, this article designs an electricity data trading method based on price game and blockchain for low-carbon power systems. It encompasses a data trading framework and the corresponding trading mechanism. The proposed trading framework contains data providers, data consumers, and a blockchain-based information system that plays the role of the data servicer to handle the transactions between data providers and consumers. The proposed trading mechanism mainly consists of three parts: 1) valuation; 2) pricing; and 3) copyrights confirmation. Those parts are executed sequentially to complete the electricity data trading process from valuation to clearing. Specially, the information theory is employed to realize multidimensional electricity data valuation. Further, the data trading game pricing is formulated as a multiobjective optimization problem considering market power constraints to solve. In addition, the digital watermarking combined with blockchain is designed to protect the electricity data copyright. With those components, the designed electricity data trading method enables the power enterprises to make profit from the low-carbon smart energy systems. Finally, experiments demonstrate the effectiveness of the proposed method.
Andrea De Salve, Alessandro Brighente, Mauro Conti
Modeling and predicting the behavior of nodes and users in blockchains provide opportunities for business strategy optimization. Indeed, the number of interactions of a node is strictly related to its balance and its prediction may be used for analytics purposes and investment strategies. However, the amount and diversity of information stored on the blockchain demand advanced tools for the modeling and analysis of blockchain data. Such tools should be able to capture the dynamicity and interaction of multiple independent actors, considering a large number of variables and dynamic interaction graph topologies. This is exacerbated by the use of smart contracts, programs stored in blockchain blocks that bring automation to blockchain’s operations and thus increasing the variability of the resulting interaction graphs. Existing modeling methodologies are unable to keep track of all these details, as they are not able to capture the temporal variability of the network. In this paper, we propose a novel framework for modeling and predicting the behavior of smart contracts on a blockchain. We propose the concept of temporal smart contracts networks, i.e., graphs representing the temporal evolution of interactions and data flow. Our framework allows the creation of temporal smart contract networks with different granularity levels by considering different interaction patterns between smart contracts, externally owned accounts, and internal transactions. Thanks to these graphs, we are able to model features such as the node in degree and amount of ether received by a smart contract, which are directly related to its behavior. We incorporate our modeling approach in Ethereum Data Inspection Tool (EDIT), a novel tool able to model interactions and predict them based on historical data. We test different machine learning models to predict features extracted by EDIT, hence allowing for the prediction of the overall behavior of the smart contract. We test EDIT on the Ethereum blockchain and model several temporal smart contracts networks, which represent the interactions and the data flow resulting from about 4 000 000 consecutive blocks. The evaluation of different real case studies shows that the proposed framework is able to predict, with a mean absolute error close to 1%, the evolution of several interesting properties (e.g., amount of received ether) related to both accounts and smart contracts.