Sathya D, S Veena, Sangamesh Ramesh Yankanchi, Soujanya Manasa
The Internet of Medical Things (IoMT), also known as healthcare IoT, consists of interconnected medical devices and applications that enable remote monitoring of patients with chronic conditions. In existing healthcare systems, data from IoMT devices is stored in the cloud for analysis. However, major challenges include ensuring data privacy and prioritising critical health information. Rapid processing and transmission of emergency health data to hospitals are crucial for timely care, while strict privacy measures are necessary to prevent risks like data breaches, fraud, and unauthorised access to medical services. To overcome these challenges, the proposed system implements Ethereum blockchain technology and an edge AI classification algorithm on data collected in real-time. Edge computing enables instant analysis, classification, and prioritisation of health data, minimising latency and facilitating quick decision-making. Simultaneously, blockchain technology ensures robust data privacy through a secure access control mechanism. Patient information is securely stored on the blockchain and accessed via an Aadhaar card number and unique tokens. These tokens enable role-based access control, allowing authorised individualsâ like doctors, nurses, patients, and relativesâto view, update, or delete specific records as needed.
Che, Zheng, Taoyu Li, Meng Shen, Hanbiao Du ¡ 5 authors
The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, which restrict their practical applicability. In this paper, we propose StealthLink, a novel framework that addresses these limitations through cross-task domain-invariant feature learning. Our key innovation lies in transferring knowledge from the well-studied domain of blockchain anomaly detection to the data-scarce task of mixing transaction tracing. Specifically, we design a MixFusion module that constructs and encodes mixing subgraphs to capture local transactional patterns, while introducing a knowledge transfer mechanism that aligns discriminative features across domains through adversarial discrepancy minimization. This dual approach enables robust feature learning under label scarcity and distribution shifts. Extensive experiments on real-world mixing transaction datasets demonstrate that StealthLink achieves state-of-the-art performance, with 96.98\% F1-score in 10-shot learning scenarios. Notably, our framework shows superior generalization capability in imbalanced data conditions than conventional supervised methods. This work establishes the first systematic approach for cross-domain knowledge transfer in blockchain forensics, providing a practical solution for combating privacy-enhanced financial crimes in decentralized ecosystems.
Michael D. Norman, Simon Brown, Mallesh M. Pai, Laurence Smith
This paper presents a novel staking coopetition design aimed at incentivizing decentralization and continuous growth of economic security within a proof-of-stake system. Staking rewards follow a nonlinear mapping relative to stake size. This affords the highest effective yields to smaller operators, fueling network growth and giving users an incentive to delegate their stake to smaller operators. This prevents the preferential accrual and centralization of stake seen in popular blockchains such as Ethereum, where popular liquid staking protocols control large fractions of the total stake thereby having outsized potential impacts on the economic security of the protocol. The proposed system addresses key challenges such as Sybil attacks and offers a comprehensive framework for future research and implementation. We introduce innovative mechanisms and gamification elements, to enhance user engagement and provide transparency in emissions.
Blockchain technology offers transformative potential for supply chain management by improving transparency, efficiency, and security. This paper proposes a framework that integrates blockchain with quality assurance, utilizing the Advanced Encryption Standard (AES) algorithm for data encryption, the Ethereum blockchain for decentralized architecture, and smart contracts for automation. Sales order data extracted from Walmartâs transactional database is encrypted using AES to secure sensitive information (e.g., client names, geographical data), then managed via Ethereum smart contracts that automate transactions, encryption/decryption, access control, and quality checks. The system leverages Ethereumâs peer-to-peer network for data validation and integrity. Computational experiments show AES achieves encryption and decryption times of 2.8 s and 3.2 s, respectively, outperforming RSA (6.7 s/7.3 s) and ABE (7.5 s/5.2 s) in efficiency and memory usage (0.0088 MB vs. 0.186 MB for RSA). Quality assurance metrics include 100% transaction traceability, 95% accuracy in automated quality checks, and 90% supplier compliance, surpassing traditional methods. This framework enhances operational efficiency, data security, and supply chain integrity, offering a scalable solution for Asset Management (AM), Enterprise Asset Management (EAM), and Supply Chain Management (SCM).
Abstractâ This study integrates blockchain technology and machine learning to enhance credit card fraud detection. â Precise fraud prediction is performed using advanced algorithms such as Random Forest, Logistic Regression, XGBoost, and Bayesian models. â Tools such as Ganache and MetaMask from Ethereum blockchain facilitate safe and transparent tracking of suspicious transactions. â Decentralized and tamper-proof properties of blockchain add reliability, and machine learning adds precision and flexibility. The system is highly accurate and transparent and has the potential to be used to fight financial fraud. â Keywordsâ Credit Card Fraud, Blockchain, Machine Learning, Ethereum, Web3, SMOTE, XGBoost, Streamlit
The cryptocurrency market is characterized by its high volatility and complex temporal dependencies, posing significant challenges for accurate price prediction. This study introduces advanced hybrid Recurrent Neural Network (RNN) architecturesâLSTM-GRU, GRU-BiLSTM, and LSTM-BiLSTMâto enhance the predictive accuracy of cryptocurrency price forecasting. By leveraging the strengths of each RNN variant, the hybrid models effectively capture intricate time-series patterns and nonlinear dependencies inherent in cryptocurrency data. The research follows a comprehensive methodology, including the collection of historical price data for Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC), rigorous data preprocessing, and the integration of hybrid architectures. Extensive experiments are conducted, and the models are evaluated using key performance metrics, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results highlight the superior performance of hybrid RNNs, with LSTM-BiLSTM excelling in BTC price prediction, GRU-BiLSTM and LSTM-GRU demonstrating robust performance for ETH and LTC. This study not only establishes the efficacy of hybrid RNN architectures for time-series forecasting but also underscores their potential for real-world applications in trading strategies. The findings set a new standard for leveraging deep learning in cryptocurrency markets, paving the way for more accurate, reliable, and adaptive forecasting systems. Future work will focus on extending this approach to a broader range of cryptocurrencies and incorporating external market factors to further enhance predictive capabilities.
Existing smart contract honeypot detection approaches exhibit high false negatives and positives due to (i) their inability to generate transaction sequences triggering order-dependent traps and (ii) their limited code coverage from traditional fuzzingâs random mutations. In this paper, we propose a hybrid fuzzing framework for smart contract honeypot detection based on taint analysis, SCH-Hunter. SCH-Hunter conducts source-code-level feature analysis of smart contracts and extracts data dependency relationships between variables from the generated Control Flow Graph to construct specific transaction sequences for fuzzing. A symbolic execution module is also introduced to resolve complex conditional branches that fuzzing alone fails to penetrate, enabling constraint solving. Furthermore, real-time dynamic taint propagation monitoring is implemented using taint analysis techniques, leveraging taint flow information to optimize seed mutation processes, thereby directing mutation resources toward high-value code regions. Finally, by integrating EVM (Ethereum Virtual Machine) code instrumentation with taint information flow analysis, the framework effectively identifies and detects security-sensitive operations, ultimately generating a comprehensive detection report. Empirical results are as follows. (i) For code coverage, SCH-Hunter performs better than the state-of-art tool, HoneyBadger, achieving higher average code coverage rates on both datasets, surpassing it by 4.79% and 17.41%, respectively. (ii) For detection capabilities, SCH-Hunter is not only roughly on par with HoneyBadger in terms of precision and recall rate but also capable of detecting a wider variety of smart contract honeypot techniques. (iii) For the evaluation of components, we conducted three ablation studies to demonstrate that the proposed modules in SCH-Hunter significantly improve the frameworkâs detection capability, code coverage, and detection efficiency, respectively.
The Ethereum blockchain has transformed decentralized finance (DeFi) and is widely used to issue ERC20 tokens. However, many of these tokens rely on unverified smart contracts, which pose serious security risks. Hackers can take advantage of vulnerabilities in these unverified ERC20 tokens, leading to scams, financial losses, and a decline in user trust. Although several tools are available to audit smart contracts, their effectiveness in analyzing unverified ERC20 tokens remains uncertain. This study examines three auditing tools HoneyBadger, Maian, and Mythril by testing how well they detect security issues in unverified ERC20 tokens. The SmartBugs framework was used to support the auditing process, enabling parallel execution, standardized reports, and bulk auditing of contracts. For a thorough evaluation, two datasets were used: one from 50,581 Ethereum blockchain blocks and another from the DappRadar list of blacklisted ERC20 tokens. These datasets were chosen to provide a broad and realistic view of how the tools perform on both typical and high-risk contracts. The tools were compared based on their ability to detect issues, their execution speed, and their overall effectiveness. The results revealed clear differences in performance: some tools were better at finding vulnerabilities accurately, while others focused more on speed than depth. This study emphasizes the need to improve smart contract auditing methods and highlights the importance of developing more effective security tools to strengthen the Ethereum blockchain.
ABSTRACT The emergence of the Metaverse has introduced significant challenges in task offloading and data processing due to its virtual universe nature with immersive environments and a multitude of interconnected users and devices. The abundance of data in the Metaverse poses security challenges in local processing, necessitating traditional methods such as data transfer to Mobile Edge Computing (MEC) and subsequently to the cloud, thereby emphasizing security concerns. In this paper, a novel approach to address these challenges has been introduced: An Ethereum Blockchainâbased MEC framework uses smart contracts designed to ensure secure task offloading. It enables authentication in the Metaverse through smart contracts, followed by modeling the task offloading issue as a Markov Decision Process (MDP). To solve this MDP problem, a hybrid algorithm integrating Deep QâNetworks (DQN) with Bidirectional Long ShortâTerm Memory (BiâLSTM), known as BRLâNet (BiâLSTM Reinforcement Learning Network), has been proposed. This framework enables secure and efficient task offloading in dynamic Metaverse environments. BRLâNet outperforms Proximal Policy Optimization (PPO), achieving a 9.93% higher reward and greater stability. The BRLâNet's performance across Blockchain consensus mechanisms shows Delegated Proof of Stake (DPoS) as the most efficient, reducing latency by 49.96%, increasing throughput by 10.48%, and lowering energy consumption by 50.24%, compared to Proof of Stake (PoS), thereby optimizing Metaverse performance.
Decentralized Finance (DeFi) has revolutionized financial transactions by enabling open, permissionless access to financial services. However, its lack of centralized oversight and pseudonymous architecture have also brought by fraudulent activities. This study presents a novel framework for fraud detection in DeFi that integrates graph neural networks (GNNs) with multi-agent reinforcement learning (MARL). Leveraging a directed transaction graph comprising 50,000 Ethereum addresses and over 120,000 token transfers, this paper evaluates four detection pipelines: extreme gradient-boosted decision trees (XGBoost), a GNN-only model (GCN), a standalone reinforcement learning agent (PPO), and a proposed GNN+RL hybrid model. The hybrid system combines graph-based embeddings with adversarial policy learning, where a fraudster and a detector co-evolve through a multi-agent PPO setup using PettingZooâs ParallelEnv. Synthetic fraud strategies are generated using a GAN and projected into the GCN embedding space to simulate adaptive threats. Experimental results show that while GCNs outperform flat-feature models, the GNN+RL hybrid achieves superior balance across accuracy (84.58%), AUC (0.8176), and F1 score (0.7493), capturing both structural and behavioral fraud signals. Reward convergence curves further illustrate emergent adversarial dynamics. The proposed framework demonstrates the effectiveness of combining relational inductive biases, dynamic decision-making, and adversarial augmentation for resilient fraud detection. Future work includes extending to cross-chain analytics and enriching contextual understanding through integration with large language models.
Blockchain is a type of decentralized distributed database. Unlike traditional relational database management systems, it does not require management or maintenance by a third party. All data management and update processes are open and transparent, solving the trust issues of centralized database management systems. Blockchain ensures network-wide consistency, consensus, traceability, and immutability. Under the premise of mutual distrust between nodes, blockchain technology integrates various technologies, such as P2P protocols, asymmetric encryption, consensus mechanisms, and chain structures. Data is distributed and stored across multiple nodes, maintained by all nodes, ensuring transaction data integrity, undeniability, and security. This facilitates trusted information sharing and supervision. The basic principles of blockchain form the foundation for all related research. Understanding the working principles is essential for further study of blockchain technology. There are many platforms based on blockchain technology, and they differ from one another. This paper will analyze the architecture of blockchain systems at each layer, focusing on the principles and technologies of blockchain platforms such as Bitcoin, Ethereum, and Hyperledger Fabric. The analysis will cover their scalability and security and highlight their similarities, differences, advantages, and disadvantages.
A S M Touhidul Hasan, Rakib Ul Haque, Larry Wigger, Anthony Vatterott
Counterfeit products cause financial losses for both the manufacturer and the enduser; e.g., fake foods and medicines pose significant risks to the publicâs health. Moreover, it is challenging to ensure trust in a productâs supply chain, preventing counterfeit goods from being distributed throughout the network. However, fake product detection methods are expensive and need to be more scalable, whereas a unified traceability system for packaged products is not available. Therefore, this research proposes a product traceability system, named Trusted Traceability Service (TTS), using Blockchain and Self-Sovereign Identity (SSI). The TTS can be incorporated across diverse industries because of its generic and manageable four-layer product packaging strategy. Blockchain-enabled SSI empowers distributed nodes, to verify them without a centralized clientâserver authorization architecture. Moreover, due to its distributed nature, the proposed TTS framework is scalable and robust, with the use of web3.0 distributed application development. The adoption of Fantom, a public blockchain infrastructure, allows the proposed system to handle thousands of successful transactions more cost-effectively than the Ethereum network. The deployment of the proposed framework in both public and private blockchain networks demonstrated its superiority in execution time and number of successful transactions.
Our study analyzes the combined impact of geopolitical risks and investor sentiment on the major cryptocurrencies, Bitcoin and Ethereum, using monthly data from December 1, 2020, to the end of April 2025. Through a rigorous econometric approach-including unit root tests (Dickey-Fuller (1979-1981) and Perron (1998)), cointegration techniques (Engle and Granger (1987) and Johansen (1990)), and error correction models (ECM and VECM)-we examined the long- and short-term dynamics between cryptocurrencies and three indices: investor sentiment, crypto market sentiment, and the composite geopolitical risk index. Our results confirm the existence of cointegration relationships between these crypto-assets and the indices, indicating structural interdependence during periods of global uncertainty. In the short term, fluctuations in investor sentiment and geopolitical risks significantly affect the returns of Bitcoin and Ethereum, with a rapid adjustment toward long-term equilibrium. Moreover, Ethereum appears to be slightly more sensitive to emotional and geopolitical shocks than Bitcoin. However, our study has certain limitations, notably the use of composite indices that may not capture all the qualitative nuances of the phenomena studied and the assumption of linearity in the modeled relationships. For future research, we suggest integrating nonlinear models and leveraging real-time sentiment data derived from artificial intelligence, as well as expanding the analysis to other segments of the crypto-asset market. Ultimately, our study enhances the understanding of exogenous factors influencing cryptocurrencies in an unstable global environment.
Team lead at Upland.me Poland, Warsaw, Poltavskyi Dmytro
This article examines the role cryptographic methods play in protecting digital assets through blockchain systems, with a particular focus on their adjustment to contemporary challenges and technological trends. An endeavor is undertaken to systematize major cryptographic algorithms, their effective appraisal in data protection, and development prospects under quantum computing threats. The study is relevant because centralized systems increasingly depend on cryptography due to greater regulatory pressures and, above all, a need for security through secrecy. The scientific novelty lies in the detailed comparative analysis of the said methodology (hashing, digital signatures, zero-knowledge proofs) for cases relating to major blockchain platforms (Bitcoin, Ethereum, Zcash), which hence demonstrate varied approaches towards security provision. The study's methodological foundation consists of analyzing 13 sources, merging a qualitative examination of algorithms and ECDSA with zk-SNARKs with a quantitative assessment of their effectiveness. Hash functions and Merkle trees ensure data integrity while reducing the computational costs of verification; asymmetric cryptography and Zero-Knowledge Proofs guarantee authenticity and confidentiality for the function of the transaction. Main findings support that cryptography is the cornerstone technology for blockchain security, but it has to be tailored to meet new challenges. Development in post-quantum algorithms and the infusion of homomorphic encryption will soon become imperative for quantum threats. This paper strongly advocates hybrid solutions that would bring traditional ways merged with novelties, which will provide sustainability over time for digital assets. Thus, this article will be useful for Developers of Blockchain Systems, Cryptographers, Cybersecurity Experts, & Regulators willing to know how protection methods for digital assets evolve.
With the promise of greater decentralization and sustainability, Ethereum transitioned from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism. The new consensus protocol introduces novel vulnerabilities that warrant further investigation. The goal of this paper is to investigate the security of Ethereum's PoS system from an Internet routing perspective. To this end, this paper makes two contributions: First, we devise a novel framework for inferring the distribution of validators on the Internet without disturbing the real network. Second, we introduce a class of network-level attacks on Ethereum's PoS system that jointly exploit Internet routing vulnerabilities with the protocol's reward and penalty mechanisms. We describe two representative attacks: StakeBleed, where the attacker triggers an inactivity leak, halting block finality and causing financial losses for all validators; and KnockBlock, where the attacker increases her expected MEV gains by preventing targeted blocks from being included in the chain. We find that both attacks are practical and effective. An attacker executing StakeBleed can inflict losses of almost 300 ETH in just 2 hours by hijacking as few as 30 IP prefixes. An attacker implementing KnockBlock could increase their MEV expected gains by 44.5% while hijacking a single prefix for less than 2 minutes. Our paper serves as a call to action for validators to reinforce their Internet routing infrastructure and for the Ethereum P2P protocol to implement stronger mechanisms to conceal validator locations.
This paper presents an implementation of a Self-Sovereign Identity (SSI) framework using Ethereum-based standards to meet the technical requirements of the European Digital Identity (EUDI) Architecture Reference Framework (ARF). By leveraging ERC-734/ERC-735 standards, the proposed eSSI system enables decentralized key management, verifiable claims, and onchain auditability. A case study on the Sepolia testnet demonstrates functional alignment with EUDI goals, while highlighting the need for enhanced privacy mechanisms such as zero-knowledge proofs for full compliance.
Existing blockchain sharding protocols have focused on eliminating imbalanced workload distributions. However, even with workload balance, disparities in processing capabilities can lead to differential stress among shards, resulting in transaction backlogs in certain shards. Therefore, achieving stress balance among shards in the dynamic and heterogeneous environment presents a significant challenge of blockchain sharding. In this paper, we propose ContribChain, a blockchain sharding protocol that can automatically be aware of node contributions to achieve stress balance. We calculate node contribution values based on the historical behavior to evaluate the performance and security of nodes. Furthermore, we propose node allocation algorithm NACV and account allocation algorithm P-Louvain, which both match shard performance with workload to achieve stress balance. Finally, we conduct extensive experiments to compare our work with state-of-the-art baselines based on real Ethereum transactions. The evaluation results show that P-Louvain reduces allocation execution time by 86% and the cross-shard transaction ratio by 7.5%. Meanwhile, ContribChain improves throughput by 35.8% and reduces the cross-shard transaction ratio by 16%.
This study compares the performance of the Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models in predicting the price of Ethereum, which is important to support cryptocurrency investment strategies. With the high volatility of the cryptocurrency market, an accurate and reliable prediction model is needed. In this study, Ethereum's daily closing price data over four years was analyzed using TCN and GRU models to evaluate its predictive capabilities. Model accuracy is measured using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The results showed that the TCN model excelled in average accuracy with lower MAE and MAPE values, while the GRU model showed excellence in reducing the impact of large errors with smaller MSE values. This reflects TCN's superiority in capturing the overall pattern of price movements, while the GRU is more responsive to short-term price fluctuations. These findings demonstrate the potential of both models in cryptocurrency price forecasting, with their respective advantages. This research provides valuable information for investors and researchers in developing predictive strategies in dynamic financial markets. A combination of TCN and GRU models can also be explored to improve prediction performance in the future.
Web3 grant programs are evolving mechanisms aimed at supporting innovation within the blockchain ecosystem, yet little is known on about their effectiveness. This paper proposes the concept of maturity to fill this gap and introduces the Grant Maturity Framework (GMF), a mixed-methods model for evaluating the maturity of Web3 grant programs. The GMF provides a systematic approach to assessing the structure, governance, and impact of Web3 grants, applied here to four prominent Ethereum layer-two (L2) grant programs: Arbitrum, Optimism, Mantle, and Taiko. By evaluating these programs using the GMF, the study categorizes them into four maturity stages, ranging from experimental to advanced. The findings reveal that Arbitrum's Long-Term Incentive Pilot Program (LTIPP) and Optimism's Mission Rounds show higher maturity, while Mantle and Taiko are still in their early stages. The research concludes by discussing the user-centric development of a Web3 grant management platform aimed at improving the maturity and effectiveness of Web3 grant management processes based on the findings from the GMF. This work contributes to both practical and theoretical knowledge on Web3 grant program evaluation and tooling, providing a valuable resource for Web3 grant operators and stakeholders.
Minh Tri Le, O. M. Harris, Charlotte Bennett, Fiona Greene
With the deep penetration of blockchain technology across various fields, its security system faces severe challenges, and fraudulent activities are becoming increasingly frequent. This study focuses on the problem of fraud detection in blockchain and proposes an innovative model, FraudGNN, based on Graph Neural Networks (GNN). The model constructs a dynamic transaction graph, where transaction addresses are treated as nodes and asset transfer relationships as edges, incorporating time-series features. A Graph Attention Network (GAT) is used to extract behavioral features from node neighborhoods. In addition, a Bidirectional Long Short-Term Memory network (Bi-LSTM) is introduced to capture behavioral paths across block-level transactions, enabling accurate classification and prediction of abnormal accounts within blockchain networks. Experiments conducted on an Ethereum transaction datasetâcontaining approximately 3.6 million transaction records and 40,000 labeled addressesâshow that the FraudGNN model significantly outperforms traditional methods such as Random Forest and Graph Convolutional Networks (GCN) in key metrics, achieving 91.2% precision, 87.5% recall, and an F1-score of 89.3%. In particular, the model demonstrates stronger generalization and reasoning capabilities when identifying previously unseen addresses, offering solid technical support for improving blockchain security systems.
ImageChain is a Python application that combines image steganography, blockchain, and Interplanetary File System (IPFS) to create a secure, decentralized system for image metadata management. The system allows users to hide secret messages in images using steganography, keeping data confidential and authentic. The images are then stored on IPFS, which creates a distributed storage hash. IPFS hash and title, description metadata are stored in a local Ethereum blockchain through Solidity-written smart contracts. Updating and retrieving metadata, as well as transferring ownership among Ethereum addresses through Web3.py, are supported by the platform. Image processing is carried out through OpenCV. With these technologies together, transparency, traceability, and tamper-evident record-keeping across the life cycle of the digital image are provided. ImageChain illustrates the value of the integration of steganography, decentralized storage, and blockchain technology to create secure systems for use in digital rights management, forensic processing, and secure communication.
Abstract Pixel-Web3 Wallet is a hierarchical deterministic (HD) wallet designed for secure and decentralized asset management across multiple blockchain networks, including Ethereum and Solana. Unlike traditional wallets that depend on browser extensions or centralized servers, Pixel offers a web-based solution with user-controlled security through locally stored seed phrases. This paper explores the walletâs architecture, security framework, and innovative features, such as real-time balance updates and flexible recovery options. Additionally, the research evaluates the scalability of Pixel and its potential expansion to support more blockchain networks. By eliminating reliance on third-party services, Pixel enhances accessibility while maintaining strong security, making it a promising solution for blockchain enthusiasts, traders, and developers. Keywords: Blockchain, HD Wallet, Cryptocurrency,Web3,Ethereum,Solana, Security
Aryan A Ayare, Vaishnavi A Jadhav, Mustafa K Banatwala, Shashank V Changlere ¡ 6 authors
Blockchain is a decentralized and distributed ledger technology that ensures data security, transparency, and immutability, making it a promising solution for academic record management. Currently, academic records are managed through centralized databases controlled by educational institutions, relying on manual processes, institutional servers, and third-party services. These systems are prone to inefficiencies, data breaches, and authentication challenges, often requiring time-consuming verification processes vulnerable to fraud. Blockchain technology addresses these limitations by offering a decentralized, tamper-proof framework that enhances security, accessibility, and trust in academic credential verification. This study reviews various blockchain platforms, consensus mechanisms, and scalability solutions, with a focus on Hyperledger Fabric and Ethereum, assessing their applicability in educational contexts. Furthermore, off-chain storage techniques like InterPlanetary File System, consensus algorithms, and access control mechanisms are analyzed to optimize the efficient and secure management of sensitive academic data. By integrating blockchain technology, educational institutions can modernize record-keeping, streamline verification processes, and enhance trust in academic credentials, ultimately creating a more secure and transparent academic record management system. Statistical analysis further highlights blockchain's growing adoption in education, demonstrating its effectiveness in reducing fraud, improving accessibility, and ensuring data integrity.
RAIMUNDO is an innovative decentralized application (DApp) designed for the legal sector, leveraging Ethereum's sustainable blockchain technology (now based on Proof of Stake) to certify documents. By using a dual-hash system, it enables attorneys to produce tamper-proof "blockchain evidence," eliminating the need for state intermediaries. This empowers legal professionals, especially in regions with authoritarian regimes or corruption, to independently certify documents. However, judicial acceptance of blockchain evidence varies. Common law systems increasingly recognize it as valid, while civil law jurisdictions, with formal and state-centric traditions, often prioritize public certification over private digital methods. Factors such as blockchain's anonymity and the strict public certification duties of European notaries contribute to this divide. Although technically compatible with notarial roles in civil law, the integration of blockchain into regulatory frameworks remains uncertain, highlighting the need for ongoing evaluation of its evidentiary value compared to traditional public documents.