Yashaswini Gokulnath V S, R Soundharya, T. B. Richard
Abstract: This article aims to address the critical need for asecured and patient-centric blockchain-based healthcare system. The research focuses on developing a hospital management system utilizing patient and doctorinformationon the Ethereum platform using a systematic approach. Smart contracts are used in Solidity to manage data. Local testing on Hardhat is conducted, and once successful the system is to be deployed on Ethereum. Security audits are performedto ensure robustness.
Tahrim Hossain, Sheikh Hassan, Faisal Haque Bappy, Muhammad Nur Yanhaona · 7 authors
Blockchain technology has revolutionized contractual processes, enhancing efficiency and trust through smart contracts. Ethereum, as a pioneer in this domain, offers a platform for decentralized applications but is challenged by the immutability of smart contracts, which makes upgrades cumbersome. Existing design patterns, while addressing upgradability, introduce complexity, increased development effort, and higher gas costs, thus limiting their effectiveness. In response, we introduce FlexiContracts, an innovative scheme that reimagines the evolution of smart contracts on Ethereum. By enabling secure, in-place upgrades without losing historical data, FlexiContracts surpasses existing approaches, introducing a previously unexplored path in smart contract evolution. Its streamlined design transcends the limitations of current design patterns by simplifying smart contract development, eliminating the need for extensive upfront planning, and significantly reducing the complexity of the design process. This advancement fosters an environment for continuous improvement and adaptation to new requirements, redefining the possibilities for dynamic, upgradable smart contracts.
Zainab S. Attarbashi, Akram M. Zeki, ME Haque, Md Hossen
ينمو إنفاق المسلمين على المنتجات الحلال بنسبة 6.3% سنويا، حيث وصل إلى 2 تريليون دولار هذا العام 2024. ويأتي توريد المنتجات الحلال من أجزاء مختلفة من العالم من مورّدين مسلمين وغير مسلمين. وقد أدى ذلك إلى زيادة قلق المستهلكين المسلمين بشأن مصداقية الادعاءات الحلال بسبب عدم القدرة على تتبع المنتجات عبر سلسلة التوريد. ومع ذلك، لا يمكن لنماذج سلسلة التوريد الحالية تتبع هذه المنتجات الغذائية طوال سلسلة التوريد من المزرعة وطوال عملية النقل. يهدف هذا البحث إلى تحقيق هدفين رئيسيين: الأول هو تقييم وعي المستهلكين بتطبيقات تقنية سلاسل الكتل (blockchain) في صناعة المنتجات الحلال، والثاني هو تطوير نموذج لامركزي لتتبع المنتجات الحلال باستخدام تقنية سلاسل الكتل لتمكين التسجيل الشفاف وغير القابل للتلاعب بالبيانات المتعلقة بالمنتجات الحلال، بما في ذلك معلومات المنشأ والجودة والتعامل والمعالجة. سيؤدي ذلك إلى تحسين شفافية سلسلة التوريد، وتعزيز كفاءة حفظ السجلات، وتعزيز سلامة الأغذية وضمان الجودة، وتمكين التتبع الشامل، وبناء ثقة المستهلك. يتضمن تطوير نموذج سلسلة التوريد الحلال استخدام العقود الذكية وتقنيات التشفير وتحليلات البيانات. تم استخدام منصة تطوير Ethereum 2.0 blockchain وWeb3.js بشكل تفاعلي لتنفيذ نموذج أولي للنظام. تظهر نتائج اختبار النظام المطبق قابلية توسع ملحوظة، وإدارة الأحمال المرتفعة دون التضحية بالكفاءة. بالإضافة إلى ذلك، أظهرت إجراءات الاختبار مدى التزام النظام بالمبادئ التوجيهية والمعايير المحددة مسبقًا، مما يعزز الثقة في الجودة العامة للنظام.
This work is devoted to the research of the blockchain network, in particular, aimed at detecting illegal activity in the Ethereum network using forensic methods. The paper describes the concepts and basic vulnerabilities related to the Ethereum network and the integration of graph analysis to develop an algorithm that scrutinizes Ethereum's transaction structure for illegal activities, including money laundering. In addition, the study includes an analysis of the very structure of Ethereum and the blockchain, which allows insight into the identification and analysis of various aspects of their functioning. The research results are used for the software implementation of the study and improvement of the security level of the blockchain network, including the creation of advanced software solutions for network analysis and protection of the integrity of the blockchain ecosystem. This integrated methodology aims to protect the integrity of blockchain ecosystems.
Open access
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
As more and more attacks have been detected on Ethereum smart contracts, it has seriously affected finance and credibility. Current anti-fraud detection techniques, including code parsing or manual feature extraction, still have some shortcomings, although some generalization or adaptability can be obtained. In the face of this situation, this paper proposes to use graphical representation learning technology to find transaction patterns and distinguish malicious transaction contracts, that is, to represent Ethereum transaction data as graphs, and then use advanced ML technology to obtain reliable and accurate results. Taking into account the sample imbalance, we treated with SMOTE-ENN and tested several models, in which MLP performed better than GCN, but the exact effect depends on its field trials. Our research opens up more possibilities for trust and security in the Ethereum ecosystem.
Antonio Jesús Chaves, Cristian Martín, Kwang Soon Kim, Adnan Shahid · 5 authors
Machine learning data privacy has been improved with Federated Learning approaches. However, some obstacles to guaranteeing traceability, openness, and participant contribution incentives prevent its widespread use. In this study, Ethereum blockchain technology is integrated into the data stream Kafka-ML framework, presenting a novel asynchronous and blockchain-based Federated Learning approach. By utilising Ethereum for transparent and auditable participant tracking, this integration overcomes some shortcomings such as auditability and model sharing reliability. Furthermore, Ethereum smart contracts allow for automatic reward distribution systems, which promote equitable incentive systems and increased involvement in the Federated Learning process. To demonstrate its potential, an extensive evaluation has been carried out on a wireless net-work technology detection use case. By improving transparency, traceability, and incentive structures of Federated Learning, it is expected to strengthen the robustness of flexible machine learning collaboration with data streams.
Phuong Duy Huynh, Son Hoang Dau, Nicholas Huppert, Joshua Cervenjak · 8 authors
We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. We first constructed two datasets of around 384,000 scammer addresses behind all one-day Simple Rug Pulls on Uniswap (Ethereum) and Pancakeswap (BSC), and identified distinctive scam patterns including star, chain, and major (scam-funding) flow. These patterns, which collectively cover about $40\%$ of all scammer addresses in our datasets, reveal typical ways scammers run multiple Rug Pulls and organize the money flow among different addresses. We then studied the more general concept of scam cluster, which comprises scammer addresses linked together via direct ETH/BNB transfers or behind the same scam pools. We found that scam token contracts are highly similar within each cluster (average similarities $>70\%$) and dissimilar across different clusters (average similarities $<30\%$), corroborating our view that each cluster belongs to the same scammer/scam organization. Lastly, we analyze the scam profit of individual scam pools and clusters, employing a novel cluster-aware profit formula that takes into account the important role of wash traders. The analysis shows that the existing formula inflates the profit by at least $32\%$ on Uniswap and $24\%$ on Pancakeswap.
In this paper, we provide an overview of a specific, but rather popular type of blockchain scaling solutions, which are usually referred to as Layer 2 blockchains.These solutions depend on the rollup scaling strategy.First, they all create batches of transactions known as rollups.Second, there are two primary approaches to rollup verifications.One approach is referred to as optimistic rollup, where it is assumed that fraudulent transactions rarely occur and should it happen, then some nodes could raise a challenge, which would lead to the resolution of the problem.The interaction between the asserter and the challenger is referred to as interactive fraud proof.The other approach is a conservative approach, where every rollup batch is associated with a validity proof.For all practical purposes, Ethereum is used as the Layer 1 blockchain because it is the first and only large-scale public blockchain that supports Turing-complete smart contracts.These Layer 2 blockchains typically deploy at least two smart contracts on the Layer 1 blockchain, one to receive rollups from the Layer 2 blockchain, and the other to verify the rollup batch.Furthermore, we provide technical details of two Layer 2 blockchains (i.e., Arbitrum and Optimism) that use the optimistic rollup mechanism, and two Layer 2 blockchains (i.e., Polygon and Starknet) that use validity rollup mechanism.Finally, we analyze the Layer 2 blockchain solutions in the framework of the blockchain trilemma theory.We show that all Layer 2 scaling solutions trade decentralization for better scalability.
Traditional voting systems face numerous challenges, including security vulnerabilities, transparency issues, and operational inefficiencies, which undermine public confidence in electoral processes.Blockchain technology offers a promising solution with its immutable, decentralized, and cryptographically secure framework, addressing these critical issues.This paper presents a blockchainbased voting system implemented across multiple Ethereum Virtual Machine (EVM) platforms, including Binance Smart Chain, Fantom, Polygon, and Celo.The system leverages smart contracts for secure vote management and Non-Fungible Tokens (NFTs) for voter authentication, ensuring the uniqueness and authenticity of each vote.Our research includes a comprehensive evaluation of the system's performance, focusing on transaction costs, processing speed, and scalability.The findings demonstrate the potential of blockchain technology to efficiently handle large volumes of electoral data while maintaining security and integrity, thereby enhancing the reliability and transparency of voting systems.
Open access
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Voting is a cornerstone of collective participatory decision-making in contexts ranging from political elections to decentralized autonomous organizations (DAOs). Despite the proliferation of internet voting protocols promising enhanced accessibility and efficiency, their evaluation and comparison are complicated by a lack of standardized criteria and unified definitions of security and maturity. Furthermore, socio-technical requirements by decision makers are not structurally taken into consideration when comparing internet voting systems. This paper addresses this gap by introducing a trust-centric maturity scoring framework to quantify the security and maturity of seventeen internet voting systems. A comprehensive trust model analysis is conducted for selected internet voting protocols, examining their security properties, trust assumptions, technical complexity, and practical usability. In this paper we propose the Internet Voting Maturity Framework (IVMF) which supports nuanced assessment that reflects real-world deployment concerns and aids decision-makers in selecting appropriate systems tailored to their specific use-case requirements. The framework is general enough to be applied to other systems, where the aspects of decentralization, trust, and security are crucial, such as digital identity, Ethereum layer-two scaling solutions, and federated data infrastructures. Its objective is to provide an extendable toolkit for policy makers and technology experts alike that normalizes technical and non-technical requirements on a univariate scale.
Smart Contracts (SCs) handle transactions in the Ethereum blockchain worth millions of United States dollars, making them a lucrative target for attackers seeking to exploit vulnerabilities and steal funds. The Ethereum community has developed a rich set of tools to detect vulnerabilities in SCs, including reentrancy (RE) and unhandled exceptions (UX). A dataset of SCs labeled with vulnerabilities is needed to evaluate the tools’ efficacy. Existing SC datasets with labeled vulnerabilities have limitations, such as covering only a limited range of vulnerability scenarios and containing incorrect labels. As a result, there is a lack of a standardized dataset to compare the performances of these tools. Our dataset, SCRUBD, aims to fill this gap. SCRUBD is a dataset of real-world SCs and synthesized SCs labeled with RE and UX vulnerabilities. The real-world SC dataset is labeled through crowdsourcing, followed by manual inspection by an experienced SC programmer, and covers both RE and UX vulnerabilities. On the other hand, the synthesized dataset is carefully crafted to cover various RE scenarios only. Using SCRUBD, we compared the performance of six popular vulnerability detection tools. Based on our study, we found that Slither outperforms other tools on a crowdsourced dataset in detecting RE vulnerabilities, while Sailfish outperforms other tools on a manually synthesized dataset for detecting RE. For UX vulnerabilities, Slither outperforms all other tools.
Smart Contracts (SCs) handle transactions in the Ethereum blockchain worth\nmillions of United States dollars, making them a lucrative target for attackers\nseeking to exploit vulnerabilities and steal funds. The Ethereum community has\ndeveloped a rich set of tools to detect vulnerabilities in SCs, including\nreentrancy (RE) and unhandled exceptions (UX). A dataset of SCs labelled with\nvulnerabilities is needed to evaluate the tools' efficacy. Existing SC datasets\nwith labelled vulnerabilities have limitations, such as covering only a limited\nrange of vulnerability scenarios and containing incorrect labels. As a result,\nthere is a lack of a standardized dataset to compare the performances of these\ntools. SCRUBD aims to fill this gap. We present a dataset of real-world SCs and\nsynthesized SCs labelled with RE and UX. The real-world SC dataset is labelled\nthrough crowdsourcing, followed by manual inspection by an expert, and covers\nboth RE and UX vulnerabilities. On the other hand, the synthesized dataset is\ncarefully crafted to cover various RE scenarios only. Using SCRUBD we compared\nthe performance of six popular vulnerability detection tools. Based on our\nstudy, we found that Slither outperforms other tools on a crowdsourced dataset\nin detecting RE vulnerabilities, while Sailfish outperforms other tools on a\nmanually synthesized dataset for detecting RE. For UX vulnerabilities, Slither\noutperforms all other tools.\n
Dongliang Cai, Liang Zhang, Borui Chen, Haibin Kan
Decentralized data sovereignty and secure data exchange are regarded as foundational pillars of the new era. Attribute-based encryption (ABE) is a promising solution that enables fine-grained access control in data sharing. Recently, Hohenberger et al. (Eurocrypt 2023) introduced registered ABE (RABE) to eliminate trusted authority and gain decentralization. Users generate their own public and secret keys and then register their keys and attributes with a transparent key curator. However, RABE still suffers from heavy decryption overhead. A natural approach to address this issue is to outsource decryption to a decryption cloud server (DCS). In this work, we propose the first auditable RABE scheme with reliable outsourced decryption (ORABE) based on blockchain. First, we achieve verifiability of transform ciphertext via a verifiable tag mechanism. Then, the exemptibility, which ensures that the DCS escapes false accusations, is guaranteed by zero knowledge fraud proof under the optimistic assumption. Additionally, our system achieves fairness and auditability to protect the interests of all parties through blockchain. Finally, we give concrete security and theoretical analysis and evaluate our scheme on Ethereum to demonstrate feasibility and efficiency.
Purpose: This study sought to determine the effect of cryptocurrency on financial market in Kenya. The study sought to specifically determine the effect of Bitcoin Ethereum, and Litecoin on financial market in Kenya. Methods: The current study adopted a qualitative research design. The specific research design qualitative research design that was adopted was a desktop research design. Results: Studies have agreed that crypto-currencies have an impact on the financial market. However, studies observes that though cryptocurrency market have a significant effect on financial market, the impact is negative. The study therefore recommends that the government must consider developing explicit regulations for cryptocurrencies. Conclusion: The government may potentially make money by levying fees on digital and online transactions, but this benefit can only be realized if the crypto business is governed by a proper legal framework.
Md. Hasibul Alam Ratul, Sepideh Mollajafari, Martín Wynn
Digital evidence plays a crucial role in cybercrime investigations by linking individuals to criminal activities. Data collection, preservation, and analysis can benefit from emerging technologies like blockchain to provide a secure, distributed ledger for managing digital evidence. This study proposes a blockchain-based solution for managing digital evidence in cybercrime cases in the judicial domain. The proposed solution provides the basis for the development of a new model that leverages a consortium blockchain, allowing secure collaboration among judicial stakeholders, while ensuring data integrity and admissibility in court. An extensive literature review demonstrates blockchain’s potential to create a more secure, efficient evidence management system. The proposed model was implemented in a test environment using a localised blockchain for developing and testing smart contracts, as well as integrating a web interface, with off-chain storage for managing evidence data. The system was subsequently deployed in both the Polygon and Ethereum test networks, simulating real-world blockchain environments, revealing that the operational cost in the Polygon network is reduced by 99.96% compared to Ethereum, thereby offering scalability without compromising security. This study underscores blockchain’s potential to revolutionise the chain of custody procedures, improving dependability and security in evidence management and providing more sustainable solutions within the criminal justice system.
Cai, Dongliang, Borui Chen, Liang Zhang, Haibin Kan
Attribute-based encryption (ABE) is a generalization of public-key encryption that enables fine-grained access control in cloud services. Recently, Hohenberger et al. (Eurocrypt 2023) introduced the notion of registered ABE, which is an ABE scheme without a trusted central authority. Instead, users generate their own public/secret keys and then register their keys and attributes with a key curator. The key curator is a transparent and untrusted entity and its behavior needs to be audited for malicious registration. In addition, pairing-based registered ABE still suffers the heavy decryption overhead like ABE. A general approach to address this issue is to outsource decryption to a decryption cloud service (DCS).In this work, we propose BA-ORABE, the first fully auditable registered ABE with reliable outsourced decryption scheme based on blockchain. First, we utilize a verifiable tag mechanism to achieve verifiability of ciphertext transformation, and the exemptibility which enables the honest DCS to escape from wrong claims is guaranteed by zero knowledge fraud proof under optimistic assumption. Additionally, our system achieves fairness and decentralized outsourcing to protect the interests of all parties and the registration and outsourcing process are transparent and fully auditable through blockchain. Finally, we give security analysis, implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, and show its advantages in real application of decentralized finance.
Mohamadsajad afkhami, Mahmoud baghani, Amir toranjsimin, Hamid‐Reza Mahrooghi
This article introduces a secure and efficient framework for the storage and validation of medical images using the Ethereum blockchain platform, incorporating the decentralized capabilities of the Interplanetary File System (IPFS) and digital signature methodologies employing zero watermarking. Medical images are critical to patient diagnosis and treatment, necessitating robust security measures, especially during transmission over insecure networks. Our approach utilizes chaotic sequences and transformations through Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD) to create a digital signature that ensures the integrity and privacy of the images without modifying their content. The solution encrypts medical images before storing them in IPFS with their corresponding digital signatures to safeguard confidentiality. Upon access, the images are decrypted and their signatures are checked to confirm their integrity. The effectiveness of this methodology is demonstrated by its strong resilience to network disturbances and potential security threats, achieving an average Normalized Correlation (NC) value of 0.97. This performance underscores the potential of integrating advanced cryptographic techniques with blockchain technology to enhance the security of medical image data.
Open access
Advanced Steganography and Watermarking Techniques
Md Zahidul Islam, Md. Shahidul Islam, Md Abdullah Al Montaser, Md Rasel · 7 authors
The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learning techniques provide the most accurate and reliable predictions under different market conditions, with a full understanding of their strengths and limitations. The dataset gathered for analyzing and forecasting cryptocurrency prices entailed diverse and extensive data points, affirming a well-rounded foundation for machine learning algorithms. Particularly, current and historic price data from cryptocurrency exchanges such as Binance, Coinbase, and Kraken, together with trading metrics important for the definition of market dynamics. Aggregated data from financial databases such as Coin-Market-Cap, Crypto-Compare, and Yahoo Finance comes in structured form and presents historical consistency, hence perfectly fitting for machine learning applications. Models considered for the study ranged from simple, linear methods to complex ensemble and gradient-boosting algorithms. Precise performance evaluation is a proxy of its reliability and correctness of effectiveness in price predictions in a cryptocurrency market. Several measures of the effectiveness of prediction have been used here for assessing the different properties of models' performance: Precision, Recall, and F1-Score. Additional performance metrics were applied to evaluate the models in this study including Mean Absolute Error, Root Mean Squared Error, and R-squared. The gradient Boosting model did an excellent job as compared to other algorithms, as the values of accuracy, precision, recall, and F1-score for both classes were quite high. All three models have quite a relatively low MAE and RMSE, which means that each model is remarkably good at predicting the target variable. The application of machine learning models in the sphere of cryptocurrency price prediction might finally give very important implications to investors and stakeholders of the financial market in the USA, especially since recently, cryptocurrencies have been made integral parts of both individual and institutional investors' portfolios and trading strategies. To investors, it may provide indications of the entry and exit points, diversification of portfolios, and risk management by using machine learning models. Consolidation with the financial system will indeed mark a strategic shift toward data-driven decision-making in investment management and trading by integrating machine learning models into the financial systems.
As decentralized applications on permissionless blockchains are prevalent, more and more latency-sensitive usage scenarios emerged, where the lower the latency of sending and receiving messages, the better the chance of earning revenue. To reduce latency, we present Pioplat, a feasible, customizable, and low-cost latency reduction framework consisting of multiple relay nodes on different continents and at least one instrumented variant of a full node. The node selection strategy of Pioplat and the low-latency communication protocol offer an elastic way to reduce latency effectively. We demonstrate Pioplat's feasibility with an implementation running on five continents and show that Pioplat can significantly reduce the latency of receiving blocks/transactions and sending transactions, thus fulfilling the requirements of most latency-sensitive use cases. Furthermore, we provide the complete implementation of Pioplat to promote further research and allow people to apply the framework to more blockchain systems.
Ahsan Adeleke Akoshile, Olamide Jogunola, Mohammad Hammoudeh, Tooska Dargahi
Recent research has exposed significant security vulnerabilities within smart contracts that run on blockchain.Threats, such as, reentrancy attacks, where malicious actors exploit recursive function calls in a smart contract, pose a critical threat.This led to substantial financial losses in organisations.Traditional vulnerability detection methods, largely based on static analysis, showed limitations in effectively identifying reentrancy issues, often yielding high false positive rates and missing complex execution paths.This paper analyses hybrid deep learning models for reentrancy vulnerability detection in Ethereum smart contracts, introducing a unique approach that combines semantic and syntactic feature extraction.Specifically, our approach integrates CodeBERT embeddings for deep semantic insights with pattern-based feature vectors that capture Solidity constructs that are vulnerable to reentrancy attacks.Five hybrid models are evaluated, each selected to provide insights into structural and sequential dependencies within code.Findings highlighted the novelty of using multimodal feature integration in vulnerability detection, with models like Autoencoder-LSTM and CodeBERT-Transformer Encoder achieving high accuracy of 98.3% and 98.01%, respectively, demonstrating the effectiveness of hybrid architectures for capturing complex vulnerability patterns.This comparative study advances the smart contract security field, showcasing each model's strengths and trade-offs, and providing practical guidance for deploying deep learning-based vulnerability detection within blockchain ecosystems.
Dhanraj Sharma, Ruchita Verma, Murad Baqis Hasan Al-Bukari, Mohammed A. K. Zaid · 5 authors
This study explores herding behavior in the cryptocurrency market during three major international crises: the COVID-19 pandemic, the Russia–Ukraine war, and the Palestine–Israel conflict. The study uses daily closing prices of five major cryptocurrencies (Bitcoin, Ethereum, Tether, BNB, and Solana) and the CRYPTO20 index data from December 31 2019 to May 20, 2024. The research employs the cross-sectional absolute deviation (CSAD) and cross-sectional standard deviation (CSSD) methods to identify herding behavior in the cryptocurrency market. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used for the robustness check. Stationarity of the data is verified using the Augmented Dickey-Fuller (ADF) test. The empirical findings reveal the anti-herding behavior in the cryptocurrency market during the three sub-periods. The study’s findings have important implications for investors, policymakers, and market regulators. Understanding the dynamics of herding behavior in the cryptocurrency market during global crises can help in developing strategies to mitigate the adverse effects of herding, such as inefficient asset pricing and increased market volatility.
Minghui Xu, Hechuan Guo, Ye Cheng, Chunchi Liu · 6 authors
Permissionless blockchains face considerable challenges due to increasing storage demands, driven by the proliferation of Decentralized Applications (DApps). This paper introduces EC-Chain, a cost-effective storage solution for permissionless blockchains. EC-Chain reduces storage overheads of ledger and state data, which comprise blockchain data. For ledger data, EC-Chain refines existing erasure coding-based storage optimization techniques by incorporating batch encoding and height-based encoding. We also introduce an easy-to-implement dual-trie state management system that enhances state storage and retrieval through state expiry, mining, and creation procedures. To ensure data availability in permissionless environments, EC-Chain introduces a network maintenance scheme tailored for dynamism. Collectively, these contributions allow EC-Chain to provide an effective solution to the storage challenges faced by permissionless blockchains. Our evaluation demonstrates that EC-Chain can achieve a storage reduction of over \(90\%\) compared to native Ethereum Geth.