The exponential growth of digital data has intensified reliance on cloud storage, yet conventional centralized architectures remain persistently vulnerable to unauthorized access, data tampering, and privacy violations. This paper presents a novel blockchain-driven approach for decentralized cloud storage that addresses these concerns through a multi-layered security mechanism. The proposed system fragments a user’s file into multiple independent blocks, encrypts each block using the AES-256 algorithm with PBKDF2-derived keys, and distributes them across distinct nodes of the InterPlanetary File System (IPFS). The corresponding IPFS hash addresses are then recorded on an Ethereum-based blockchain through a Solidity smart contract, ensuring immutability and tamper-resistance of the entire storage index. During file retrieval, the system queries the blockchain to collect all block hash addresses, fetches encrypted blocks from IPFS, reassembles them in the correct sequence, and delivers the decrypted output to the user. The implementation is built using Python, Django, Web3.py, and the Truffle/Ganache development environment, and has been functionally verified across all core user-facing modules.
Intherapidlyevolvingdigitallandscape,freelancing platforms face significant challenges due to a lack of transparency,trust,andcentralizedcontrol.Thispaperpresents the design and implementation of a blockchain-powered web- based project management system integrated with a visual data dashboard. The proposed system leverages Ethereum smart contractstoensuresecure,tamper-proofuserregistration,project posting, bidding,assignment, work submission, payment release, and rating. The backend is developed using Django, while blockchain integration is achieved via Web3.py, enabling secure and transparent interactions. The platform provides real-time analyticsonusers,jobstatus,fundmovement,andratingsthrough a dashboard. The solution enhances trust, transparency,and de- centralization,provingeffectiveforfreelanceprojectecosystems
This study focuses on preventing unauthorized recipient transactions within the Ethereum blockchain system. Unauthorized recipient transactions occur when a sender transfers cryptocurrency without the recipient's awareness, posing risks such as the recipient being implicated in crimes such as suspected involvement in money laundering. Previous research has designed a transaction restriction function using smart contracts tailored to Ethereum's unique blockchain model. This prevention mechanism was implemented on open-source software and its functionality verified. This study proposes a method to enhance the security of processing conducted to investigate the relationship between senders and receivers. We implement this method in open-source software and demonstrate its effectiveness.
Reentrancy remains one of the most critical classes of vulnerabilities in Ethereum smart contracts, yet widely used detection tools and datasets continue to reflect outdated patterns and obsolete Solidity versions. This paper adopts a dependability-oriented perspective on reentrancy detection in Solidity 0.8+, assessing how reliably state-of-the-art static analyzers and AI-based techniques operate on modern code by putting them to the test on two fronts. We construct two manually verified benchmarks: an Aggregated Benchmark of 432 real-world contracts, consolidated and relabeled from prior datasets, and a Reentrancy Scenarios Dataset (RSD) of \chadded{143} handcrafted minimal working examples designed to isolate and stress-test individual reentrancy patterns. We then evaluate 12 formal-methods-based tools, 10 machine-learning models, and 9 large language models. On the Aggregated Benchmark, traditional tools and ML models achieve up to 0.87 F1, while the best LLMs reach 0.96 in a zero-shot setting. On the RSD, most tools fail on multiple scenarios, the top performer achieving an F1 of 0.76, whereas the strongest model attains 0.82. Overall, our results indicate that leading LLMs outperform the majority of existing detectors, highlighting concerning gaps in the robustness and maintainability of current reentrancy-analysis tools.
The objective of this work is to present a decentralized healthcare records management system. The system is built on the Ethereum blockchain using Solidity smart contracts and a React-based web interface. And this system addresses security, integrity, and privacy limitations of traditional centralized electronic health record (EHR) systems. By enforcing the role-based access control, immutable storage of patient records, and transparent audit trails for all operations. A smart contract ‘HealthcareRecords’ manages patient data operations and provider authorization, while a MetaMask-integrated frontend enables secure interaction with the blockchain through an intuitive interface. The local Hardhat Ethereum network is used to deploy the proposed work, outcome of the prototype demonstrates a secure healthcare record creation and retrieval of the patient data. This local network demonstrating the strict access control, low gas consumptions and low latency, which are suitable for real usages in healthcare environments.
The next generation of financial and economic infrastructure has been realized by Real-World Asset (RWA) tokenization, which represents physical and regulated assets on distributed ledgers. Regardless of increased institutional interest, its large-scale adoption is limited by the scalability, regulatory compliance, governance, and finality of settlement issues intrinsic to traditional systems. This paper suggests a fractional asset tokenization model based on ERC-1155 on the Hedera blockchain using its Permissioned-public governance system, deterministic finality, and native token services. The proposed system will enhance the liquidity of assets, their accessibility to the market, and their efficiency and profitability by facilitating compliant fractional ownership, which is consistent with the changing regulatory processes. When compared to Ethereum and Bitcoin, it shows that Hedera would be more appropriate to the requirements of institutional grade RWA tokenization.
P. Saranya, A. W. Ali, Meesala Shobha Rani, H. Shaheen
The current land transfer system in India is beset with inefficiencies, delays and increased costs due to its reliance on manual processes, extensive paperwork and involvement of multiple intermediaries such as real estate agents and government officials. Discrepancies and verification challenges arise from land records maintained in physical ledgers or decentralized digital formats across various government departments, often leading to fraudulent transactions, disputed ownership claims and unauthorized land sales. The limited transparency and access to land records, further exacerbate corruption and undermine trust in the system. This chapter explores how blockchain technology and smart contracts can revolutionize the land transfer system in India by addressing these inherent challenges. The emergence and growing popularity of blockchain technology is mainly due to the success and influence of cryptocurrencies like Bitcoin and Ethereum. Ethereum has become the backbone of the decentralized finance sector, further driving its adoption and market perception. Blockchain&s;s decentralized and immutable ledger ensures the authenticity and security of ownership data, while smart contracts automate the entire land transfer process, reducing the need for intermediaries and minimizing human error. The integration of these technologies fosters transparency by providing real-time access to unified land records for all stakeholders, significantly reducing the risk of fraud. Additionally, the streamlined process can greatly reduce the cost and time associated with land transactions. By providing a clear, tamper-proof chain of ownership, blockchain technology also aids in dispute resolution, offering a transformative solution for modernizing land management in India. The proposed work aims to enhance property ownership by creating smart contracts with the terms of the land sale, including the agreed price, property details and conditions for transfer. It verifies the ownership of the seller and checks for any encumbrances or legal issues with the property. This can be done automatically by querying the blockchain ledger. Once the verification is done, the payment can be transferred and the ownership records would be updated on the blockchain ledger,transferring the ownership rights to the buyer and providing a tamper proof and transparent record of the transaction.
Integrating blockchain into the Industrial Internet of Things (IIoT) has emerged as a promising solution for preserving data privacy and ensuring IoT security. Among various blockchain platforms, Ethereum stands out due to its support for smart contracts and its interoperability with lightweight communication protocols. Despite these advantages, particularly within Ethereum-based networks, IIoT systems remain vulnerable to large-scale threats such as Sybil attacks. These attacks pose a critical security risk because an adversary generates numerous fake entities to infiltrate and compromise the network, ultimately undermining its integrity and availability. Existing approaches utilize Ethereum smart contracts and lightweight protocols such as MQTT to secure IIoT communications, but often overlook sophisticated threats such as Sybil attacks, which introduce fraudulent nodes into the network. Conventional detection methods typically depend on centralized monitoring, undermining scalability and privacy, and there remains a lack of publicly available datasets representing adversarial behaviors in IIoT environments. In this paper, an Ethereum-based IIoT network is first developed, and a publicly available dataset is released through the GitHub repository. An advanced method is then proposed to detect and prevent Sybil attacks in a PoA-based IIoT network using decentralized federated learning. During the detection phase, a convolutional neural network (CNN) is employed within the decentralized federated learning framework, achieving an average detection accuracy and recall of 91.13% and 91.37% among clients, respectively. In the prevention phase, a secure smart contract is designed to manage a dynamic reputation system, effectively preventing Sybil nodes from remaining active on the network.
In this study, it is aimed to compare quantitative forecasting methods (traditional and learning based) in cryptocurrency market. For his purpose the daily prices between 16 September 2017 – 15 September 2022 of Bitcoin, Ethereum, Binance Coin and Monero were analyzed with five different methods: ARIMA, exponential smoothing, artificial neural networks, RNN and LSTM.In the results it is indicated that exponential smoothing method is the most successful method at forecasting daily prices. The method has high performance in forecasting BTC, ETH and BNB daily prices. But at forecasting daily XMR prices, artificial neural networks method was the most successful one.The other point which was detected in this study is deep learning based methods made some unsuccessful forecasts. This is thought to be due to the fact that deep learning methods require more data. In future studies, using other quantitative methods (e.g. GRU, XGBoost, transformer models) on other cryptocurrencies will contribute to the literature.
Venkatesh Babu R, Vignesh D, Sibaath Ahmed S, M P Ramkumar · 5 authors
The conventional messaging platform such as WhatsApp or Telegram is based on a centralized server, which fundamentally creates a gateway to censorship, surveillance, and points of failure. In essence, that is damaging to the user privacy and information security. In this paper, therefore, we develop and implement a Decentralized Chat Application (DCA) using Ethereum blockchain. Based on the fundamental capabilities of distributed ledger technologies, namely, immutability, transparency, and trustlessness, we are building a reliable, censorship-resistant chat service. The application operates under Ethereum Smart Contracts to handle decentralized user identities and to store public encryption keys safely as well as establishing chat channels. Our practical message content and media flows are stashed into an effective Peer-to-Peer (P2P) network, potentially stashed in off-chain storage such as Interplanetary File System(IPFS), but all messages are End-to-End Encrypted.The DCA model provides a good framework of the next-gen secure, private, and autonomous social interaction, as it leaves the end-users with complete ownership and control over their digital communication by abandoning the central authority.
Blockchain technology has emerged as a secure and decentralized solution for data management across various domains. However, existing consensus mechanisms face challenges related to security, scalability, and energy efficiency, while blockchains remain vulnerable to sophisticated attacks such as double spending, selfish mining, and Sybil attacks. This paper proposes a novel hybrid blockchain security framework that integrates a Hybrid Consensus Algorithm (HCA) combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) with Machine Learning based attack detection. The hybrid consensus improves transaction finality and reduces energy consumption, while the ML module detects anomalous behaviors in real time. Experimental evaluation using a private Ethereum based blockchain demonstrates that the proposed approach improves attack detection accuracy up to 96.8 %, reduces consensus latency by 34 %, and enhances throughput by 27 % compared to traditional PoW based systems. The results confirm that integrating hybrid consensus with intelligent security mechanisms significantly strengthens blockchain resilience.
Konduri S P S Narayana Murthy, Tirumala Anand Kumar, R.Nivedha, B Balasaigayathri
In this paper, I introduce a decentralized freelancing site that combines blockchain-based smart-contract escrow with AI-based matching of talents to improve the level of trust, transparency, and efficiency in digital labor markets. Conventional freelancing models make use of centralized middlemen, which introduce vulnerability to the system in the form of payment conflicts, recommendations of jobs, manipulation of data and single point failure. The suggested system will resolve these concerns by implementing an unchanging Ethereum smart-contract escrow, which automates the processes of hiring, funds locking, and milestone payments without the involvement of third parties. The hybrid backend application with FastAPI, the SQLite data store, and the Web3 interaction provides the security of the requests processing and the state of the contract retrieval in real-time. Also, there is an AI ranking module with TF-IDF vectorization and cosine similarity that offers personalized matching of freelancers and jobs based on skill-relevant and experience characteristics. An up-to-date React/Tailwind frontend provides an easy user flow of posting jobs, hiring freelancers, depositing escrow funds, and approving task completion. Experimental analysis using live contract deployment shows that the transparency of transactions is more optimal, the probability of dispute is lower, and the accuracy of the matching is much higher. This site is an example of how the next-generation decentralized freelance ecosystems can be practiced.
Solana has experienced rapid growth due to its high performance and low transaction costs, but the extremely low barrier to token issuance has also enabled widespread Rug Pulls. Unlike Ethereum-based Rug Pulls, which often rely on malicious smart-contract logic, Solana's unified SPL Token program shifts fraudulent execution toward on-chain behavioral manipulation. However, existing research has not systematically examined these Solana-specific Rug Pull patterns, and no public Solana Rug Pull dataset is available for empirical research. To bridge this gap, we present a large-scale measurement study of Rug Pulls on Solana. We manually verify 68 community-reported incidents and curate a benchmark of 117 confirmed Rug Pull tokens, from which we distill three representative on-chain behavioral patterns: Freeze Authority Abuse, Liquidity Withdrawal, and Pump-and-Dump. Guided by these patterns, we design a behavior-guided candidate identification and human-validation pipeline. We apply this pipeline to 100,063 tokens newly issued on Orca, Raydium, and Meteora during the first half of 2025, identifying 76,469 Rug Pull tokens. A random manual audit of 382 samples estimates a labeling false-positive rate of 0.26\%, supporting the reliability of the dataset. We release the resulting dataset and use it to characterize the Solana Rug Pull ecosystem. Our analysis shows that Rug Pulls on Solana exhibit extremely short lifecycles, strong price-driven dynamics, severe economic losses, and highly organized group behaviors. These findings provide new insights into the Solana Rug Pull landscape and support the development of effective on-chain defense mechanisms.
Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a focus on financial records. The model introduces the concept of an issuer-bound parent token and document-linked child tokens, enforcing a structured trust relationship between a legitimate institution and the documents it issues. By combining on-chain cryptographic hashing with off-chain file references, the approach is designed to balance verifiability with scalability. We implement a proof-of-concept using Ethereum-compatible smart contracts on a permissioned blockchain and evaluate it in a consortium-style financial setting. Our functional analyses demonstrate the model’s ability to ensure document integrity, provenance, and resistance to document fraud. This work offers a practical and extensible foundation for secure digital document authentication and verification in financial and other trust-sensitive settings.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Blockchain technology is often discussed as if it emerged from nowhere, yet its architectural DNA traces directly to the decentralized computing principles James~N. Gray articulated in 1986. This paper maps the conceptual lineage from Gray's requestor/server model to modern blockchain architectures, showing how his emphasis on modularity, autonomy, data integrity, and standardized communication anticipated the design of systems like Bitcoin and Ethereum, and, more recently, the Web3 movement and Layer-2 scaling architectures. We examine consensus mechanisms, cryptographic foundations, rollup-based Layer-2 protocols, and cross-chain interoperability through this historical lens, identify persistent challenges in scalability and modularity, and outline future directions toward Web4: an intelligent, decentralized internet integrating blockchain, artificial intelligence, and the Internet of Things.
Umar Majeed Sada, Abrar Ahmad, Najam Saqib, Md. Sarwar
Introduction: With the increasing adoption of solar photovoltaic, bioenergy, and wind energy in distributed networks, consumers are transforming into prosumers, utilizing renewable energy resources within distributed microgrid systems. In these environments, peer-to-peer (P2P) energy trading emerges as a highly promising energy management solution. Methods: This paper introduces a Polygon-based blockchain smart contract designed to execute trading and payment rules autonomously, significantly enhancing the security and fairness of energy trading compared to traditional database technologies. The smart contract consists of two core components: a bidding and settlement module and a payment module, both deployed on a Polygon-based test network, Cardona, to assess real-time interactions. Furthermore, to validate this model, simulations were conducted using realistic data on an Ethereum Virtual Machine (EVM) in a microgrid setting with six participants. Results: The results illustrate the model's potential to facilitate the integration of renewable energy within microgrids, thereby encouraging the adoption of distributed energy resources and supporting localized P2P energy trading. The proposed methodology shows that Polygon-based smart contracts with auction mechanisms can enable efficient, transparent, and automated P2P energy trading, enhancing grid stability in non-autonomous microgrids. However, the approach requires validation at larger scales to assess performance under real-world conditions . Conclusion: This approach offers a robust framework for designers aiming to build sustainable, decentralized energy markets.
In this paper, we presented an e-Registry prototype that builds on the decentralized and tamper-evident nature of Ethereum to authenticate digital documents. The system is a gas-efficient Smart Contract on the Ethereum blockchain that stores SHA-256 hashes of documents, so your sensitive files are secure with us, but we don't know what they are! Accessible through a web-based user interface using MetaMask for transaction signing and the Web Crypto API for client-side hashing of documents offers an intuitive privacy-preserving process. Finally, system diagnostics and security reports can be generated using Python-based tools and hashed to store on the blockchain for validation. Running on the Ethereum Sepolia testnet, the system provides practical efficiency, with transaction fees of 0.0005-0.001 SepoliaETH and verification delay less than 3 seconds. This provides an alternative to traditional centralized verification systems that suffer from single points of failure, lack of transparency, and reliance on third-party services by offering a secure and tamper-resistant decentralized proof of existence for digital files.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
This paper introduces a novel architecture that brings the Algorithmic Contract Types Unified Standards (ACTUS) to Ethereum by combining on-chain contract definition with off-chain deterministic computation and on-chain, oracle-mediated settlement. The design encodes ACTUS terms and life-cycle state in an ERC-20 stablecoin with balance locks, while a whitelisted multi-oracle computes cash-flows and settles them without manual intervention. This approach gives DeFi instruments bank-grade, standardized cash-flow semantics and auditable automation at low cost. In a three-year time-warp over a portfolio we measure \(\approx\) 60,000 gas per settlement ( \(\approx\$0.08\) ), with oracle throughput around 240 contracts/s on commodity hardware. Initial deployment is \(\approx\$3.74\) , and per-contract setup \(\approx\$0.48\) .
Peer-discovery protocols within P2P networks are often vulnerable: because creating network identities is essentially free, adversaries can eclipse honest nodes or partition the overlay. This threat is especially acute for blockchains, whose security depends on resilient peer connectivity. We present AetherWeave, a stake-backed peer-discovery protocol that ties network participation to deposited stake, raising the cost of large-scale attacks. We prove that, with high probability, either the honest overlay remains connected or a $(1{-}δ)$-fraction of nodes in every smaller component raise an attack-detection flag -- even against a very powerful adversary. To our knowledge, AetherWeave is the first peer-discovery protocol to simultaneously provide Sybil resistance and privacy: nodes prove they hold valid stake without revealing which deposit they own, and gossiping does not expose peer-table contents. A cryptographic commitment scheme rate-limits discovery requests per round; exceeding the limit yields a publicly verifiable misbehavior proof that triggers on-chain slashing. Beyond deposit and slashing, the protocol requires no on-chain interaction, with per-node communication scaling as $O(s\sqrt{n})$. We validate our design through a mean-field analysis with closed-form convergence bounds, extensive adversarial simulations, and an end-to-end prototype built by forking Prysm, a leading Ethereum consensus client.
Arti Patle, Shubham Bora, Gaurav Salunke, Suyash Biradar
In the current cyber digital era, financial fraud has evolved into a sophisticated threat that often bypasses conventional detection systems. Fraudsters exploit fake accounts and unregulated payment gateways, making it challenging for legacy systems to keep up. To address these modern threats, Mule Trace offers an intelligent and real-time fraud detection framework. It extends its capabilities by integrating blockchain technology, specifically Ethereum, for logging suspicious activities, ensuring transparency and immutability of flagged data. Utilizing machine learning models such as Isolation Forest and Gaussian Mixture Models (GMMs), Mule Trace is capable of identifying irregularities in financial transactions with improved accuracy and minimal false positives. The platform operates in real time through a Web3.js interface, removing reliance on centralized systems and enhancing system resilience. Coupled with a React.js dashboard, users can visualize transactions, detect anomalies, and respond promptly to threats. Mule Trace thus provides a robust, scalable solution for modern financial institutions to combat illicit financial behaviors.