A Smart Contract (SC) is a digital negotiation process between two or more anonymized participants without trusted middlemen. It is an autonomous SC manifested as software code. SC operates on the blockchain (BC). The program and the SC are immutable and preserved on a decentralized public registry. Many potential applications for SC exist within the digital economy, encompassing financial services, administration, medical care, and the Internet of Things (IoT). Ethereum and Hyperledger constitute the most prevalent open-source advanced cross-industry BC systems for cross-border collaboration. Significant technological concerns, including security, privacy, accuracy, and verifiability, remain unresolved and require further maturation in BC. This study thoroughly examines SC. The research provides a case study on a university test system characterized by a heterogeneous data structure. This implementation provides an in-depth comprehension of the SC architecture and has been utilized to identify and evaluate the deficiencies in the current state of SC technology.
Francisco J. Díaz, Carolina Menchaca, Lukas Weidener
Introduction The scientific community is increasingly interested in leveraging decentralized technologies to address systemic challenges such as the reputation economy, the monopolization of academic publishing, and the replication crisis. This study presents an analysis of the Decentralized Science (DeSci) landscape in 2023, focusing on organizational structures, technological foundations, and funding mechanisms of DeSci organizations. Methods A 16-question survey was distributed to DeSci organizations between December 2023 and April 2024, and responses from 49 projects were analyzed using quantitative and qualitative methods. Results Results highlight the prominent role of Ethereum as the dominant blockchain platform in DeSci, the varied applications of blockchain in scientific processes, and a significant emphasis on community building and infrastructure development. Funding sources within the ecosystem are moving towards partnerships with more traditional organizations, including academia. However, most projects lack DAO features for governance. It remains uncertain whether they will adopt more DAO-like structures in the future or deploy a different organizational model. Discussion Our findings offer a comprehensive overview of the progress and challenges facing the DeSci ecosystem, including slow project progression due to leadership issues and limited funding for most DeSci projects. By identifying key patterns and areas for improvement, this study contributes to a deeper understanding of the factors driving success and sustainability in DeSci.
This publication presents a novel cryptographic commit scheme named DFTWS which is used to enable deterministic, fair, and transparent winner selection in an open source Proof-of-Useful-Work blockchain for High Energy Physics (HEP) called gophy. In gophy, instead of spamming hashing operations to mine blocks, miners are running computationally expensive Monte Carlo simulations to support a real-world HEP experiment with necessary data required to conduct the experiment. To preserve the usefulness property, block problems are defined over time by a Root Authority which is coordinated by a representative of a real-world HEP experiment. In order to be able to provide a transparent mechanism that allows for fair block winner selection from a list of eligible miners that solved a block problem, DFTWS is employed to achieve consensus between nodes. A strength of this approach is that every node is able to verify the fairness of the winner selection process. This publication provides an in-depth description and theoretical fairness analysis of DFTWS, a practical evaluation of its performance under real-world conditions and considerations of potential bottlenecks that can potentially occur as the node network scales. It also discusses a deployment strategy for using DFTWS on top of existing blockchain infrastructure like the Ethereum network. Additionally, theoretical performance aspects of DFTWS are compared with various state-of-the-art cryptographic commitment schemes and Zero-Knowledge Proof systems.
Endrizal Ridwan, Mailinda Tri Wahyuni, Dwi Fitrizal Salim
This study aims to analyze the impact of news related to Central Bank Digital Currency (CBDC) on the stock and cryptocurrency markets in the United States. Using the Time-Varying Parameter Vector Autoregression (TVP-VAR) method, this study examines the responses of the S&P 500 index, the CBOE Volatility Index (VIX), Bitcoin trading volume, and Ethereum trading volume to CBDC news from January 2020 to December 2023. The results indicate that CBDC news has a positive effect on stock market prices, but its impact on market volatility is negligible. Furthermore, Bitcoin and Ethereum trading volumes exhibit a declining trend in response to the rapid development of CBDC news. Although CBDC development is still in its early stages, these findings provide insights into the potential influence of CBDC news on financial market behavior, particularly in shaping investor sentiment and digital asset trading patterns. The study suggests that policymakers and investors should closely monitor CBDC developments, as they may gradually affect the financial ecosystem. Future research should further explore the long-term effects of CBDCs on financial market stability and cryptocurrency adoption.
Ahmad Jurnaidi Wahidin, Rayhan Maulana Sugiharto Putra
Penelitian ini mengkaji potensi investasi dalam cryptocurrency dengan menerapkan metode Net Present Value (NPV) sebagai sistem pendukung keputusan untuk menilai dan membandingkan tiga cryptocurrency yaitu Bitcoin (BTC), Ethereum (ETH), dan Binance Coin (BNB). Cryptocurrency telah muncul sebagai instrumen investasi yang menarik perhatian besar dalam beberapa tahun terakhir, terutama di kalangan milenial, karena dianggap sebagai mata uang masa depan. Dalam penelitian ini, kinerja ketiga cryptocurrency dianalisis berdasarkan tiga kriteria utama: Tingkat Pertumbuhan Tahunan (Annual Growth Rate), Volume Perdagangan (Trading Volume), dan Ketersediaan di Platform Pertukaran Terkemuka (Availability on Major Exchange Platforms). Data dikumpulkan dari berbagai sumber tepercaya seperti bursa cryptocurrency dan laporan keuangan. Perhitungan NPV dilakukan untuk mengukur nilai sekarang dari arus kas masa depan yang diharapkan dari masing-masing cryptocurrency. Hasil penelitian menunjukkan bahwa Binance Coin (BNB) memiliki nilai NPV tertinggi sebesar $29.416,91, diikuti oleh Ethereum (ETH) dengan NPV sebesar $26.085,74, dan Bitcoin (BTC) dengan NPV sebesar $22.948,93. Ini menunjukkan bahwa BNB menawarkan nilai investasi terbaik di antara ketiga cryptocurrency yang dianalisis, berdasarkan kriteria yang ditetapkan. Penelitian ini menjadi sistem pendukung bagi investor untuk membuat keputusan investasi yang lebih informasional dan beralasan dalam pasar cryptocurrency yang fluktuatif.
Phishing is a serious threat to cryptocurrency networks; Bitcoin and Ethereum are prime targets for these attacks. This paper discusses some aspects of phishing attacks on these platforms. While the simpler architecture of Bitcoin leads to more direct phishing attempts, the more complex ecosystem in Ethereum introduces a wide range of attack vectors through dApps and smart contracts. A comparative analysis of phishing attacks in both blockchains shows that while both have their fair share of attacks, Bitcoin seems to bear the brunt of phishing attacks. Current defense strategies, like 2FA and anti-phishing tools, as well as recommendations for increasing network security against phishing are discussed in this paper. Understanding these phishing mechanisms is crucial in strengthening the security of blockchain platforms and mitigating future attacks.
Scaling blockchain performance through parallel smart contract execution has gained significant attention, as traditional methods remain constrained by the performance of a single virtual machine (VM), even in multi-chain or Layer-2 systems. Parallel VMs offer a compelling solution by enabling concurrent transaction execution within a single smart contract, using multiple CPU cores. However, Ethereum's sequential, shared-everything model limits the efficiency of existing parallel mechanisms, resulting in frequent rollbacks with optimistic methods and high overhead with pessimistic methods due to state dependency analysis and locking.
Ben Berger, Edward W. Felten, Akaki Mamageishvili, Benny Sudakov
Optimistic rollups rely on fraud proofs -- interactive protocols executed on Ethereum to resolve conflicting claims about the rollup's state -- to scale Ethereum securely. To mitigate against potential censorship of protocol moves, fraud proofs grant participants a significant time window, known as the challenge period, to ensure their moves are processed on chain. Major optimistic rollups today set this period at roughly one week, mainly to guard against strong censorship that undermines Ethereum's own crypto-economic security. However, other forms of censorship are possible, and their implication on optimistic rollup security is not well understood. This paper considers economic censorship attacks, where an attacker censors the defender's transactions by bribing block proposers. At each step, the attacker can either censor the defender -- depleting the defender's time allowance at the cost of the bribe -- or allow the current transaction through while conserving funds for future censorship. We analyze three game theoretic models of these dynamics and determine the challenge period length required to ensure the defender's success, as a function of the number of required protocol moves and the players' available budgets.
The ``EIP-1599 algorithm'' is used by the Ethereum blockchain to assemble transactions into blocks. While prior work has studied it under the assumption that bidders are ``impatient'', we analyze it under the assumption that bidders are ``patient'', which better corresponds to the fact that unscheduled transactions remain in the mempool and can be scheduled at a later time. We show that with ``patient'' bidders, this algorithm produces schedules of near-optimal welfare, provided it is given a mild resource augmentation (that does not increase with the time horizon). We prove some generalizations of the basic theorem, establish lower bounds that rule out several candidate improvements and extensions, and propose several questions for future work.
Penelitian ini menganalisis data historis cryptocurrency menggunakan metode Exploratory Data Analysis (EDA) untuk membantu investor pemula memahami pergerakan harga mata uang kripto. Cryptocurrency, sebagai uang digital yang tidak berwujud fisik, memiliki tingkat volatilitas tinggi yang sering menyebabkan kerugian bagi investor yang kurang berpengalaman dalam menganalisis data historis. Menggunakan dataset dari CoinMarketCap yang terdiri dari 679.183 baris dan 13 kolom periode 2017-2022, penelitian ini menerapkan metodologi EDA dengan pendekatan visualisasi data. Prosedur penelitian mencakup analisis masalah, data acquisition melalui web scraping, data cleaning, dan exploratory data analysis. Hasil analisis menunjukkan persaingan antara Bitcoin dan Ethereum. Berdasarkan marketval, Bitcoin mencapai $140.000.000.000, sementara Ethereum $80.000.000.000. Volume Bitcoin mencapai $8.000.000.000, sedangkan Ethereum $4.000.000.000. Analisis price movement menunjukkan Ethereum mencapai $140.000, sementara Bitcoin $1. Dalam analisis moving average, Ethereum menunjukkan performa lebih baik dengan grafik mencapai $105.000, dibandingkan Bitcoin yang hanya mencapai $0,8. Penelitian ini berkontribusi dalam membantu investor pemula memahami dinamika pasar cryptocurrency melalui analisis data historis. Hasil visualisasi dan analisis dapat digunakan sebagai acuan pengambilan keputusan investasi dan meminimalisir risiko kerugian. Studi ini merekomendasikan penggunaan data historis sebagai alat prediksi dibandingkan pengambilan keputusan berbasis intuisi dalam investasi cryptocurrency.
Smart contracts are programs that permanently store and automatically execute on the blockchain system such as Ethereum. Due to the non-tamperable nature of the underlying blockchain, smart contracts are difficult to update once deployed, which requires redeploying the contracts and migrating the data. It means that the observation of smart contract evolution in the real world makes more sense. Hence, in this paper, we conducted the first large-scale empirical study to characterize the evolution of smart contracts in Ethereum. For evolution identification, we presented a contract similarity-based search algorithm, digEvolution, and evaluated its effectiveness with five different search strategies. Then we applied this algorithm to 80,152 on-chain contracts we collected from Ethereum, to dig out the evolution among these contracts. We then explored three research questions. We first studied whether the evolution of smart contracts is common (RQ1), then we studied how do the Gas consumption (RQ2) and the vulnerability (RQ3) of smart contracts vary during the evolution. Our research results show that the evolution of smart contracts is not very common. There are some contract components that have vulnerability but still be called by users. The Gas consumption of most smart contracts doesn’t vary during the evolution, contract is Gas-efficient before and after the evolution. The vulnerability of most smart contracts doesn’t vary during the evolution, both are secure before and after the evolution.
Large language models (LLMs) have transformed code generation. However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity language, the predominant programming language for Ethereum smart contracts. Due to the lack of adequate benchmarks for Solidity, LLMs' ability to generate secure, cost-effective smart contracts remains unexplored. To fill this gap, we construct SolEval, the first repository-level benchmark designed for Solidity smart contract generation, to evaluate the performance of LLMs on Solidity. SolEval consists of 1,507 samples from 28 different repositories, covering 6 popular domains, providing LLMs with a comprehensive evaluation benchmark. Unlike the existing Solidity benchmark, SolEval not only includes complex function calls but also reflects the real-world complexity of the Ethereum ecosystem by incorporating Gas@k and Vul@k. We evaluate 16 LLMs on SolEval, and our results show that the best-performing LLM achieves only 26.29% Pass@10, highlighting substantial room for improvement in Solidity code generation by LLMs. Additionally, we conduct supervised fine-tuning (SFT) on Qwen-7B using SolEval, resulting in a significant performance improvement, with Pass@5 increasing from 16.67% to 58.33%, demonstrating the effectiveness of fine-tuning LLMs on our benchmark. We release our data and code at https://github.com/pzy2000/SolEval.
Blockchain technology, characterized by its decentralized, immutable, and transparent nature, opens up transformative opportunities for enhancing accounting systems. Traditional accounting practices typically depend on centralized databases, which can be vulnerable to data manipulation, fraud, and security breaches. This paper proposes an innovative accounting system built on the Ethereum public blockchain, designed to effectively manage critical functions such as inventory management, customer interactions, sales transactions, and invoicing. By leveraging smart contracts, this system automates key processes—including invoicing and payments—resulting in reduced manual intervention and fewer human errors while improving operational efficiency. The decentralized framework of blockchain guarantees that every transaction is permanently recorded in an immutable ledger, offering unparalleled security and transparency. Additionally, this system reduces reliance on intermediaries, streamlining business operations and decreasing costs. However, challenges such as scalability and fluctuating transaction (gas) fees still persist within the Ethereum ecosystem. As Ethereum continues to evolve with the introduction of Layer 2 scaling solutions and Ethereum 2.0, these issues are anticipated to be resolved, positioning blockchain as a revolutionary solution for the accounting sector.
The research paper will focus on the influence of major cryptocurrencies, especially Bitcoin and Ethereum, on world financial markets and traditional financial systems. It looks at how, because of their decentralized nature, these digital assets have brought new dynamics to financial markets in the price of other assets, their volatility, and their means of investment. The research design is of a mixed-methods nature, combining quantitative data from financial market indices with qualitative insights from expert interviews. Some of the main lessons learnt are declining value with other financial assets, the interdependency between movements in crypto assets and other linked assets and disruptions in banking, payments and investment. Besides, there are regulation decisions that should consider the fluctuations of the market and security requirements, as well as the analysis of many initiatives in order to provide sufficient regulation frameworks on the international level. The concluding advice proposed how not only to accommodate the disturbance of current financial stability through innovations but also to integrate the utilization of cryptocurrencies.
Independent Researcher, San Francisco, CA, USA, Sahaj Tushar Gandhi
Smart contracts, which allow for decentralized, automated transactions on blockchains, have been the source of repeated financial loss from hacking and coding flaws. This article introduces an AI-based deep learning approach to automated detection of vulnerabilities in smart contracts on Ethereum. The architecture integrates code- token embeddings (CodeBERT-style), control- and data-flow graph representations, and a hierarchical graph neural network (HGNN) with attention-based multimodal fusion to allow for comprehensive understanding of human-written programs. We train on labelled datasets from real-world contracts, utilising data augmentation and addressing class imbalance (focal loss + over sampling). For the experimental study, we compare the performance of our framework with existing solely-static and sequence-based transformers approaches apart from other GNN models on public datasets; ScrawlD, SmartBugs and manually curated Github-derived samples. Results The fused HGNN model performs with an average F1-score of 0.91, precision of 0.89, recall of 0.93 and AUC of 0.95 better than transformer- only (F1 = 0.86) and static-tool baselines (F1 = 0.71). The method shows strong generality to a wide range of vulnerability forms (reentrancy, integer overflow, unchecked calls, access control bugs) and enhances the precision for function-level localization. We further develop an interpretation module to map attention weights back to AST/CFG regions for human auditors. The paper also addresses limitations on dataset bias, obfuscation-resilience and adversarial examples and provides ideas for further investigation such as few-shot adaptation with one-class VAEs, integration with continuous deployment pipelines. The contributions: a multimodal deep-learning model for vulnerability detection and localization, an empirical study on state-of-the-art performance in multiple benchmark projects with large amounts of code; and advice how to deploy the AI-assisted contract auditing in development workflows.
In traditional stock market, the global processing framework shares the data to various mediators like financial and government institutions. The institutional firms needs to handle with large number of data in the system and cooperates with others to provide the trades on the stock exchange platform, and consistently buy-sell orders pass through various parties before settlement. It involves a complex chain of intermediaries, has several drawbacks like weak transparency, long lead times for financial settlements, and a single point of failure. Blockchain (BC) computer node network securely shares the common ledger without intermediaries. This paper uses a deep learning-based Smart contract in the private ethereum consortium blockchain (PEC-BC) to provide financial security. First, the data is collected. Then it is given to the next stage. The Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA) is used to choose the leader block. Further, the selected new leader block is used in the Adaptive Deep Temporal Context Networks (ADTCN) with a consensus algorithm to make secured smart contracts. Here, the parameters are optimized by DB-BOA. The developed ADTCN-based financial security system was compared with other conventional methods, and algorithms performed well.
H.C. Zhang, Shike Li, Shike Li, Hang Bao · 6 authors
The rapid development of blockchain technology has driven the widespread application of decentralized applications (DApps) across various fields. However, DApps cannot directly access external data and rely on oracles to interact with off-chain data. As a bridge between blockchain and external data sources, oracles pose potential risks of malicious behavior, which may inject incorrect or harmful data, leading to trust and security issues. Additionally, with the surge in data requests, the disparity in oracle trustworthiness and costs has increased, making the dynamic selection of the most suitable oracle for each request a critical challenge. To address these issues, this paper proposes a Trust-Aware and Cost-Optimized Blockchain Oracle Selection Model with Deep Reinforcement Learning (TCO-DRL). The model incorporates a comprehensive trust management mechanism to evaluate oracle reputation from multiple dimensions and employs an improved sliding time window to monitor reputation changes in real time, enhancing resistance to malicious attacks. Moreover, TCO-DRL uses deep reinforcement learning algorithms to dynamically adapt to fluctuations in oracle reputation, ensuring the selection of high-reputation oracles while optimizing node selection, thereby reducing costs without compromising data quality. We implemented and validated TCO- DRL on Ethereum. Experimental results show that, compared to existing methods, TCO-DRL reduces the allocation rate to malicious oracles by more than 39.10% and saves over 12.00% in costs. Furthermore, simulated experiments on various malicious attacks further validate the robustness and effectiveness of TCO-DRL
Blockchain is a decentralized digital ledger that records transactions across a distributed network of computers, enabling secure and transparent operations without requiring trust in a central authority. While initially developed for Bitcoin, blockchain technology now underpins many cryptocurrencies and other applications. It serves as an open trust layer without central reliance and is widely used in cryptocurrencies such as Bitcoin and Ethereum. However, this public and permanent open storage has raised concerns about its potential misuse for illegal trades or the distribution of unwanted content. In EuroS&P 2017, Ateniese et al. introduced the concept of the redactable blockchain, which utilizes the trapdoor collision function provided by chameleon hash to rewrite block contents without causing hashing inconsistencies. Recent research has continued to propose solutions for redactable blockchains, leveraging cryptographic algorithms such as chameleon hash and attribute-based encryption (ABE). Current solutions often employ sophisticated cryptographic schemes, such as ABE, but lack sufficient focus on developing secure and scalable solution for practical use. In this work, we propose the time-verifiable policy-based chameleon hash (TPCH) as a candidate solution for practical redaction to rewrite blockchain contents. Our solution for redactable blockchains enables the verification of whether a redaction was executed at a specific time, thereby offering time-based traceability for dominant algorithms in TPCH. Additionally, it restricts misbehavior or abuse of redaction powers by introducing a new trapdoor finding algorithm, Update, in addition to the adapt algorithm Adapt. We formally introduce TPCH with both black-box and white-box constructions. Our experimental and theoretical analysis demonstrates the feasibility and practicality of the proposed solution.
Within the framework of high-frequency volatility modeling, this study investigates the realized volatility spillover dynamics across major cryptocurrencies over an extended period of time. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model of the realized volatility (RV), this work constructs the Total Connectedness Index (TCI) and Pairwise Connectedness Index (PCI) to measure the intensity and direction of realized volatility transmission within this digital asset network. Our findings reveal a consistently high level of spillovers among these leading cryptocurrencies, with notable peaks during periods of global market turbulence. Notably, Ethereum emerges as the most influential volatility transmitter, challenging the traditional view of Bitcoin as a primary driver of volatility spillovers. This reflects Ethereum’s pivotal role in decentralized finance (DeFi), decentralized applications (dApps), and its growing trading activity, suggesting a shifting influence in the increasingly diversified cryptocurrency ecosystem.
In smart contract fuzz testing, it is crucial to consider the inter-dependencies between the contract functions. To effectively test the business logic of a contract, its functions must be invoked in a meaningful order. In this paper, we propose techniques that utilize static analysis on Ethereum bytecode to tackle this challenge. When compared with the current state-of-the-art, our approach takes Solidity compiler’s variable packing optimization into account and allows more precise analysis of the data-flows between functions. In addition, we devise a novel test case initialization algorithm for fuzz testing, which minimizes the redundancy in the generated seed set. Our algorithm reduces test cases that share similar function call patterns and leads to more effective testing of the contract code during the fuzz testing. Experimental results show that the proposed techniques improve the effectiveness of smart contract fuzz testing for vulnerability detection. Specifically, our techniques enabled the fuzz testing tool to trigger the target bugs in the benchmark 3.0 times faster on average.
Abdul Khalique Shaikh, Naresh Adhikari, Amril Nazir, Abdul Salam Shah · 6 authors
<ns3:p>Background Ensuring the security and trustworthiness of a digitized and automated electoral process remains a significant challenge in democratic systems. As digital voting systems are increasingly being investigated around the world, ensuring the integrity of the process using robust security measures is of great importance. This paper presents a simplified model to enhance electoral integrity by leveraging Blockchain technology in the context of Oman’s digital voting system. The model uses Blockchain technology to create a secure and trustworthy voting environment, addressing key vulnerabilities in digital electoral systems. Methods The research utilized a quantitative approach, employing an experimental design methodology using open-source software to simulate voting systems. Synthetic population data is utilized for operating these systems, while advanced biometric authentication technologies are used to verify voter identities. Blockchain technology is leveraged to ensure secure vote recording, with smart contracts used to authenticate voters and securely record votes. Additionally, synchronous transactions are executed for both voter registration and voting processes, enhancing the overall security and efficiency of the system. Results The experimental results shows that Blockchain enhances electoral integrity and security in Oman voting system, improves transparency and reliability in elections. The performance evaluation of the model focuses on efficiency, reliability, and scalability metrics. Asynchronous transactions are utilized to improve processing time for voter registration and voting. Election administrators can manage, monitor, and certify election results, while Ethereum nodes ensure decentralized verification and transparency in the voting process. Conclusion This research offers insights for policymakers to consider Blockchain for electoral reforms, addressing issues like data integrity, fraud prevention, and transparency to boost voter trust. A strong regulatory framework and public awareness are crucial for successful implementation. Pilot projects are needed to assess Blockchain’s practical impact. Oman could lead global innovation in electoral technology, though infrastructure and public resistance challenges must be managed.</ns3:p>
Md. Sameeruddin Khan, Tom Chen, Mithileysh Sathiyanarayanan, Mohammed Mujeerulla · 5 authors
The Internet of Things (IoT) model is presented in this paper with multi-layer security based on the Lenstra-Lenstra-Lovasz (LLL) algorithm. End nodes for the Internet of Things include inexpensive gadgets like the Raspberry Pi and Arduino boards. It is not practical to run rigorous algorithms on them, as opposed to computer systems. Therefore, a cryptography procedure is required that could function on this IOT equipment. Bitcoins and Ethereum are examples of cryptocurrency and Ripple employs techniques such as elliptic curve digital signature, Elliptic-Curve Diffie-Hellman (ECDH), and algorithm to sign any cryptocurrency on SECP256k1 elliptic curves transactions. By using Lenstra-Lenstra-Lovasz on a real-world Bitcoin blockchain and applying it to multiple dimensions, such as nonce leakage and weak nonces across several elliptic curves with different bit sizes on a Raspberry Pi, we can demonstrate the security of elliptic curve cryptosystems. Public key encryption techniques are seriously threatened by the development of quantum computing. Therefore, employing lattice encryption with Nth Degree Truncated Polynomial Ring Units (NTRU-NTH) on the Bitcoin blockchain will increase the resistance of Bitcoin blocks to quantum computing assaults. The execution time taken on SECP256k1 is 131.7 Milli seconds comparatively faster than NIST-224P and NIST-384P.
Yin, Chaoyue, Mingzhe Li, Jin Zhang, Lin You · 6 authors
With the development of Ethereum, numerous blockchains compatible with Ethereum's execution environment (i.e., Ethereum Virtual Machine, EVM) have emerged. Developers can leverage smart contracts to run various complex decentralized applications on top of blockchains. However, the increasing number of EVM-compatible blockchains has introduced significant challenges in cross-chain interoperability, particularly in ensuring efficiency and atomicity for the whole cross-chain application. Existing solutions are either limited in guaranteeing overall atomicity for the cross-chain application, or inefficient due to the need for multiple rounds of cross-chain smart contract execution. To address this gap, we propose IntegrateX, an efficient cross-chain interoperability system that ensures the overall atomicity of cross-chain smart contract invocations. The core idea is to deploy the logic required for cross-chain execution onto a single blockchain, where it can be executed in an integrated manner. This allows cross-chain applications to perform all cross-chain logic efficiently within the same blockchain. IntegrateX consists of a cross-chain smart contract deployment protocol and a cross-chain smart contract integrated execution protocol. The former achieves efficient and secure cross-chain deployment by decoupling smart contract logic from state, and employing an off-chain cross-chain deployment mechanism combined with on-chain cross-chain verification. The latter ensures atomicity of cross-chain invocations through a 2PC-based mechanism, and enhances performance through transaction aggregation and fine-grained state lock. We implement a prototype of IntegrateX. Extensive experiments demonstrate that it reduces up to 61.2% latency compared to the state-of-the-art baseline while maintaining low gas consumption.