The blockchain technology is sweeping the globe. Blockchain has emerged as a disruptive technology for the future generation of multiple industrial applications because to its decentralised, transparent, and secure nature. Cloud of Things, which is possible by the marriage of cloud computing with the Internet of Things, is one of them. Considering the need for security and efficiency as a problem, this paper proposes a safe and efficient smart home design that combines blockchain and cloud computing technologies to provide a comprehensive solution. The decentralised nature of blockchain technology allows it to provide processing services and create transaction copies of obtained sensible user data from smart homes. Blockchain, a distributed ledger technology that provides an immutable log of transactions recorded on a distributed network, has lately gained popularity as the underlying technology of cryptocurrencies and is revolutionising data storage and processing in computer network systems. Blockchain is seen as a possible option for future data-driven networks (DDNs) to provide safe data storage, sharing, and analytics, user privacy protection, strong, trustworthy network governance, and decentralised routing and resource management
Blockchain technology promises to revolutionize payment systems, yet high transaction costs and network congestion remain significant barriers. This paper introduces WePay, a novel blockchain payment solution that achieves up to $87 \%$ reduction in transaction costs through an innovative gas optimization framework. Our system implements a proprietary transaction batching algorithm, non-custodial cross-chain execution model, and adaptive fee structure that outperforms existing solutions across key metrics. Benchmarking results demonstrate WePayâs superior performance, processing $\mathbf{1, 2 0 0}$ transactions per minute compared to $300-500$ for traditional methods, while maintaining robust security guarantees. User testing with 50 participants revealed a $92 \%$ satisfaction rate and significantly improved adoption potential compared to current blockchain payment systems.
Haolin Huang, Basem Suleiman, Ali Anaissi, Muhammad Johan Alibasa
Predicting cryptocurrency prices has become increasingly challenging due to their limited trading history and pronounced price volatility. Similar to traditional stock markets, where investor sentiment is significantly influenced by news and social media interactions, Twitter has recently emerged as a prominent indicator of Bitcoin price movements. This study investigates the influence of notable tweets on Bitcoin price fluctuations. In this paper, we present a method to extract hourly and daily impactful tweets related to Bitcoin by leveraging the Twitter network's topology. Furthermore, we propose a Bitcoin price prediction approach that consists of (a) a Twitter sentiment index model, constructed using data from influential tweets, and (b) a time-series XGBoosting model utilising Bitcoin price features derived from historical price data. Through an experimental evaluation, conducted on a substantial dataset comprising 1.8 million tweets, 400k Twitter users, and two months of Bitcoin price data, our model demonstrated superior performance, achieving a MAPE of 0.56%, an R-Square value of 0.99, a Pearson Correlation Coefficient of 0.99, and a Direction Accuracy of 92.06%. Based on the feature importance analysis of our model, we identify that the sentiment index and Bitcoin trade volume wield significant influence over Bitcoin price dynamics. The two-month time range may limit generalization to other market conditions.
With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional machine learning models have shown to be effective in fraud detections, they often compromise user data by requiring centralized access to sensitive information. In IoT-enabled financial endpoints such as ATMs and POS Systems that regularly produce sensitive data that is sent over the network. Federated Learning (FL) offers a privacy-preserving, decentralized model training across institutions without sharing raw data. FL enables cross-silo collaboration among banks while also using cross-device learning on IoT endpoints. This survey explores the role of FL in enhancing financial security and introduces a novel classification of its applications based on regulatory and compliance exposure levelsâ ranging from low-exposure tasks such as collaborative portfolio optimization [16] to high-exposure tasks like real-time fraud detection [7], [8]. Unlike prior surveys, this work reviews FLâs practical use within financial systems, discussing its regulatory compliance and recent successes in fraud prevention and blockchainintegrated frameworks. However, FLâs deployment in finance is not without challenges. Data heterogeneity, adversarial attacks, and regulatory compliance make implementation far from easy. This survey reviews current defense mechanisms and discusses future directions, including blockchain integration, differential privacy, secure multi-party computation, and quantum-secure frameworks. Ultimately, this work aims to be a resource for researchers exploring FLâs potential to advance secure, privacycompliant financial systems.
Xiaoxue Zhang, Sammy Tesfai, Minmei Wang, Haofan Cai
Multi-enterprise applications in fields like supply chain management, finance, and healthcare require complex collaboration and data exchange among organizations to ensure operational efficiency and build trust. Permissioned blockchains emerged as a promising solution, providing shared, immutable ledgers that enhance transparency, traceability, and trust among authorized parties. However, during asset trading between organizations, they must verify the legitimacy of asset transfers, including asset ownership and quantity, while protecting sensitive asset owner information. To achieve both verifiability and privacy, this paper introduces PAVE, Privacy-preserving Aggregated Verification system for Multi-Enterprises Blockchain, a framework that integrates zero-knowledge proofs to enable secure asset verification without breaking user privacy. To achieve proof efficiency, PAVE introduces a proof aggregation mechanism that consolidates multiple transaction verifications into a single proof, significantly reducing computational overhead for large-scale scenarios. Evaluation results show that, with the proof aggregation mechanism, PAVE achieves low verification latency and resource utilization, making it a scalable solution for privacy-preserving asset verification across multiple enterprises.
With the rapid expansion of the digital economy ecosystem, blockchain has become the core technology and theoretical path to support data interaction and trusted transactions. However, it still faces significant challenges in terms of privacy protection, transaction traceability and anti-attack capabilities. To address the above challenges, this study proposes a dynamic trust-aware blockchain security algorithm (DTBCSA) based on differential privacy and zero-knowledge proof. This algorithm is used to improve the system robustness and privacy protection capabilities in multiple scenarios. The core design of the algorithm includes: (1) introducing a dynamic trust evaluation mechanism. This mechanism dynamically adjusts the trust level of the node by analyzing its behavioral characteristics and historical interactions. At the same time, the secondary authentication mechanism is activated when the risk threshold is triggered; (2) embedding a differential privacy mechanism in the computing power transaction and model training process. This mechanism protects sensitive data and model parameters through Laplace noise; (3) using zero-knowledge proof to ensure the non-repudiation of transactions. At the same time, the aggregation and parallel optimization strategies are adopted to significantly reduce the computational overhead of proof and verification. In the experimental part, DTBCSA reduced the acceptance rate of malicious transactions from 70% of the baseline to 8% in the computing power trading market simulation. At the same time, it reduced the reputation distribution imbalance (Gini) from 0.42 to 0.20. In the federated learning collaborative training experiment, DTBCSA reduced the success rate of member inference attacks from 81% to 38% under the condition of privacy budget Îľ=1. While maintaining privacy protection, it improved the model accuracy by about 1.8 percentage points compared to FedDP. In addition, in the zero-knowledge proof verification performance test, DTBCSA reduced the proof size and generation time through aggregation optimization.
La tesi indaga come le primitive crittografiche sostengano sicurezza e integritĂ di Bitcoin, coniugando teoria e pratica. Si parte dai fondamenti (riservatezza, integritĂ , autenticazione, non ripudio) e dalle basi di complessitĂ computazionale che giustificano la âone-waynessâ degli algoritmi moderni. Vengono presentate cifratura simmetrica e asimmetrica, funzioni hash e lâalgoritmo SHA-256 (double hashing), con cenni alla minaccia quantistica e agli standard post-quantum in via di adozione. Sul piano applicativo si descrive lâarchitettura: blockchain come registro append-only, Merkle tree e Merkle root per verifiche efficienti, gestione di chiavi e indirizzi; firme digitali ECDSA e lâevoluzione SegWit/Taproot con Schnorr e MAST, che riducono malleabilitĂ e ingombro on-chain migliorando privacy ed efficienza. La sezione operativa tratta HD wallet (seed phrase, derivation paths) e schemi avanzati di firma a soglia, evidenziandone impatti su usabilitĂ e rischio. La sicurezza di rete è analizzata attraverso i principali vettori dâattacco (double spending, 51%, address poisoning), il ruolo degli incentivi economici del mining e il retarget della difficoltĂ che stabilizza il tempo di blocco. Per la privacy si distinguono pseudonimia e anonimato e si valutano tecniche on/off-chain: CoinJoin/PayJoin, Stonewall(x2), Dandelion++ e Lightning Network; si discutono anche Zero-Knowledge Proofs e Self-Sovereign Identity con DIDs/VCs e cornice eIDAS. Infine si affronta la scalabilitĂ : trilemma sicurezza-decentralizzazione-throughput, ottimizzazioni on-chain (SegWit) e soluzioni Layer-2 (Lightning, sidechain), insieme alla governance degli aggiornamenti tramite soft e hard fork. Conclusione: un modello modulare in cui il Layer 1 resta strato di regolamento sicuro, mentre Layer-2 e nuove primitive crittografiche abilitano efficienza, privacy e resilienza nel lungo periodo.
Luigi Coppolino, Giovanni Maria Cristiano, Salvatore DâAntonio, Jonah Giglio ¡ 6 authors
Ensuring the reliability and accuracy of information is a critical challenge in sectors like finance, media, and health. The reliance on centralized verification systems introduces risks of bias, manipulation, and limited transparency. To address these issues, we propose VeriNet , a decentralized framework for third-party content verification leveraging blockchain technology and the Ethereum Attestation Service. VeriNet integrates on-chain and off-chain attestations to ensure privacy, transparency, and accountability, supported by a Decentralized Data Warehouse and cryptographic Proof-of-SQL mechanisms. The framework includes Contributors , who submit content, and Verifiers , who assess its authenticity. We carried out two Proof-of-Concept implementations, namely in deepfake detection and fintech credit scoring, to demonstrate the efficiency of VeriNet and its adaptability to diverse domains. Moreover, we conducted an experimental evaluation focusing on various parameters, such as costs and execution time, to demonstrate framework feasibility, scalability, and potential to establish a trusted ecosystem for collaborative verification.
The increasing demand for cloud computing services has led to the rapid expansion of cloud data centers, which consume significant amounts of energy and contribute substantially to global CO2 emissions. As the IT industry grows, the environmental impact of these data centers becomes an urgent concern. Green Cloud Computing (GCC) has emerged as a solution to mitigate this impact by focusing on energy efficiency and reducing carbon footprints while maintaining the necessary functionality and performance of cloud infrastructures. However, traditional blockchain consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) face limitations regarding energy consumption and scalability, which exacerbates the environmental burden. This study proposes a quantum-inspired blockchain consensus algorithm designed to optimize energy consumption and reduce latency in cloud data centers. By integrating quantum principles such as superposition and entanglement, the algorithm enhances task scheduling and resource utilization, enabling more energy-efficient operations without sacrificing performance. Simulations in a green cloud environment showed that the quantum-inspired algorithm resulted in up to a 30% reduction in energy usage compared to traditional consensus methods, with a 40% improvement in consensus processing time. These results suggest that quantum-inspired algorithms hold significant potential for enhancing the sustainability of cloud infrastructures by improving energy efficiency and scalability. Furthermore, this study discusses the feasibility of implementing quantum-inspired algorithms on classical hardware, addressing challenges in scalability and integration into existing blockchain frameworks. The findings provide valuable insights into the potential of quantum-inspired technologies to drive energy-efficient solutions in cloud computing.
This research tackles the challenges of non-independent and identically distributed (non-IID) data, socio-political inequalities in decentralized energy networks, and the unpredictable nature of renewable energy sources. It integrates blockchain technology with federated learning (FL) and game theory. Cluster-based FL paired with Shapley value allocation helps mitigate data heterogeneity, while a combined Stackelberg-Shapley model implemented via smart contracts facilitates adaptive pricing strategies. To safeguard user privacy, the system incorporates zero-knowledge proofs, differential privacy$(\varepsilon=0.5)$, and CKKS-based homomorphic encryption, achieving a 98% resistance rate against cyberattacks. Field tests in the EU's NER400 sandbox and blockchain-enabled microgrids in Kenya confirm the framework's effectivenessâachieving a 4.2% mean absolute percentage error (MAPE) in forecasting (improving from a 12% benchmark), curbing renewable energy certificate (REC) fraud by 89%, and cutting rural energy expenses by 40%. Leveraging a hybrid consensus model (PBFT with Sharding), the platform supports over 10,000 per second with sub-second latency, bridging interoperability gaps between Ethereum-based REC systems and Hyperledger platforms.
Mario Norberto Sevilio de Oliveira Junior, http://lattes.cnpq.br/0962672305508249
O presente trabalho realiza uma anĂĄlise comparativa de desempenho de contratos inteligentes implementados em duas arquiteturas distintas de blockchain: a Ethereum Virtual Machine (via Binance Smart Chain) e a Bitcoin Virtual Machine (via Bitcoin Satoshi Vision). O estudo teve como objetivo avaliar o impacto das diferenças arquiteturais no custo de transaçþes, no tempo de execução e na ocupação de rede, a fim de identificar vantagens e limitaçþes prĂĄticas de cada plataforma. Para alcançar esse propĂłsito, foram desenvolvidos dois contratos inteligentes de referĂŞncia: um contrato de mensagem, representativo de operaçþes simples de armazenamento, e um contrato de contador, concebido para simular cenĂĄrios de maior complexidade lĂłgica e de estresse computacional. Ambos foram implementados em linguagens especĂficas das plataformas (Solidity para BSC e sCrypt para BSV) e executados em ambientes de teste controlados. A metodologia incluiu a configuração de infraestrutura experimental, a realização de testes de desempenho, a medição de custos, bem como a execução de funçþes de ataque para avaliar a escalabilidade sob alta carga.
Selvaragavan S, Karuppasamy L, Yuvan Sankar NKR, R Sylaja Vallee Narayan S. ¡ 6 authors
The Internet of Things (IoT) has significantly transformed critical domain such has healthcare, transportation, industry, and smart cities by connecting billions of devices that can gather and share data. However, as it grows rapidly, IoT systems are grappling with significant issues around data security, user privacy, and trust. Centralized systems are particularly at risk due to single points of failure, latency issues, and potential privacy breaches, which makes them less ideal for large-scale use. While blockchain technology provides a way to decentralize and resist tampering, traditional frameworks can be too resource-heavy and impractical for low-power IoT devices.This paper introduces a Lightweight Blockchain Framework designed for Secure and Privacy-Preserving IoT Systems. It combines energy-efficient consensus mechanisms, trust-based reputation management, and a hybrid approach to on-chain and off-chain storage. By utilizing edge-assisted processing, we can cut down on computational demands, and cryptographic techniques like elliptic curve cryptography and zero-knowledge proofs help maintain privacy during authentication. The framework is built with scalability, low latency, and effective communication in mind, making it suitable for real-time IoT applications.Simulation results show that our proposed framework can reduce communication costs by 35%, lower latency, and improve privacy protection compared to traditional blockchain-based IoT systems. These findings indicate that the framework is not only lightweight and secure but also practical for applications that need privacy and quick responses, such as in healthcare, industrial automation, and smart infrastructure.
Aiming at the problems of single-point failure, privacy leakage, and high communication delay existing in the process of massive intelligent terminals accessing the new power system with traditional centralized identity authentication methods, this paper proposes an efficient identity authentication method for power terminals based on blockchain and non-interactive zero-knowledge proof. By improving Schnorr protocol, a dynamic random number driven non interactive authentication mechanism is designed to avoid the high delay of private key transmission and multi round communication. At the same time, in combination with the distributed ledger characteristics of the blockchain, the terminal public key and authentication records are decentralized stored, eliminating the dependence on a single CA. In this paper, we propose an aggregate signature method, which aggregates the zero knowledge proofs of multiple devices into a total signature, reducing the computation and communication overhead when authenticating a large number of terminal devices. This paper analyzes the performance of the proposed method using building simulation blockchain on the Hyperledger Fabric platform. Compared with other methods, this method performs well in the actual authentication phase, and reduces the time cost by more than 4.5%. Through batch certification test, compared with single terminal certification, the time cost is reduced by more than 80%. The security analysis results show that this method can resist replay attacks, phishing attacks, etc., and ensure the identity anonymity of terminal devices, the confidentiality of private keys, and the integrity of data transmission.
G Jayashree, M. Anitha, R Chethana, Prateek Prasanna Savanur
Blockchain technology has emerged as a revolutionary force in the digital era, redefining transactional security and data integrity. This paper presents a blockchain-based solution to enhance the security and transparency of crowdfunding platforms using Ethereum smart contracts. Traditional crowdfunding platforms are marred by security vulnerabilities, lack of transparency, high transaction fees, and reliance on intermediaries. Our proposed system leverages Ethereum's decentralized architecture, smart contracts, and Polygon network scalability, coupled with the Interplanetary File System (IPFS) for decentralized storage. The platform automates fund disbursement based on milestone verification, mitigating fraud risks and enabling global participation. Performance evaluation demonstrates the system's efficiency, with transaction costs reduced by 30% and milestone-based fund disbursement achieving 98% accuracy compared to traditional systems.
The speedy development of the Internet of Things (IoT) needs safe and efficient and scalable data transmission systems that can resist privacy violation and inefficiency within the network. The paper introduces a blockchain-based IoT transmission framework that inculcates Paillier Homomorphic Encryption (PHE) to end-to-end data transmission security as well as Practical Byzantine Fault Tolerance (PBFT) consensus mechanism as a low-latency trust establishment methodology. The model has been written in Python and tested with the Intel Lab IoT Sensor Dataset. The proposed system has an average success rate in secure data encryption of up to 99.97 percent and acceptable percentages of bit error with tolerance limits on all packet lengths (1 KB and above) as depicted in experimental results, thus with low computation needs (3.2 ms encryption, 3.4 ms decryption) and moderate memory consumption (120 KB). The network performance analysis proves that smaller block sizes (5 KB) provide 245 Tx/s throughput using 0.2 J energy, thus, being utilized in real-time IoT operation. PBFT protocol achieves high levels of latency (90 ms) and finality (1.1 s) reduction than the Proof-of-Work and Proof-of-Stake, and uses up to 96 percent less energy than PoW. Using homomorphic encryption with lightweight consensus is a good compromise between security, scalability, and achievable energy efficiency that fits well in next-generation IoT deployments where trust, privacy, and performance are of utmost importance.
With the rapid development of the Internet of Things (IoT), the security and privacy of personal data has received widespread attention. Federated learning models protect personal privacy data through distributed collaborative training models, but it has been shown that personal privacy data can be inferred from uploaded parameters. Federated learning models also face the challenges of privacy leakage risk, computational inefficiency and lack of verifiability. Existing differential privacybased federated learning models and homomorphic encryptionbased federated learning models are unable to balance model accuracy and security. They also face the problem of inefficient computation of client-side local data and high communication overhead. Therefore, in this paper, we propose a federated learning framework (RGC-FL) based on Re-randomizable Garbled Circuits (RGC), which achieves a balance between privacy protection and computational efficiency through dynamic encryption and re-randomization techniques. The model updates are first encrypted at the client using the obfuscated circuits and then uploaded to the server, and then the ciphertext updates are aggregated by the re-randomization technique to avoid the leakage of the original data. Secondly, the client verifies the correctness of the serverâs aggregation results by zero-knowledge proof. Finally based on DDH assumption and Kilian randomization technique to defend against hybrid attacks in dynamic input scenarios. We experimentally show that the model accuracy of RGC-FL on MNIST and CIFAR-10 datasets is 97.3% and 83.9%, respectively, which is close to plaintext federated learning and significantly outperforms the Differential Privacy (DP-FL) and Fully Homomorphic Encryption scheme (FHE-FL). In terms of efficiency, the training time for a single round is only 32% of that of FHE-FL (12.4 sec vs. 38.7 sec), and the communication overhead is reduced by $80 \%(5.2 \mathrm{MB}$ vs. 25.6 MB). This paper provides an efficient and secure solution for federated learning in highly privacy-sensitive domains and promotes the wide application of AI under compliance requirements.
This paper presents an enhanced blockchain-based salary certification system that leverages FISCO BCOS, Node.js, and MySQL to address critical challenges in traditional salary management systems. The system introduces more substantial advancements in blockchain topology design, performance optimization, and security mechanisms. It features a hierarchical consortium blockchain architecture that balances decentralization with operational efficiency, ensuring robust security and transparency. The system employs advanced techniques such as dynamic validator set management, batch transaction processing, and parallel validation pipelines to achieve higher transaction throughput and reduced latency. Additionally, it integrates zero-knowledge proofs and a multi-active data center architecture for enhanced data protection and disaster recovery. Performance evaluation demonstrates significant improvements in transaction throughput and latency, with the system achieving high success rates in both open and query type tests. The modular design allows for flexible deployment across various organizational structures, providing a comprehensive solution for secure, transparent, and efficient salary data management.
Bitcoin's limited scripting capabilities and lack of native interoperability mechanisms have constrained its integration into the broader blockchain ecosystem, especially decentralized finance (DeFi) and multi-chain applications. This paper presents a comprehensive taxonomy of Bitcoin cross-chain bridge protocols, systematically analyzing their trust assumptions, performance characteristics, and applicability to the Artificial Intelligence of Things (AIoT) scenarios. We categorize bridge designs into three main types: naive token swapping, pegged-asset bridges, and arbitrary-message bridges. Each category is evaluated across key metrics such as trust model, latency, capital efficiency, and DeFi composability. Emerging innovations like BitVM and recursive sidechains are highlighted for their potential to enable secure, scalable, and programmable Bitcoin interoperability. Furthermore, we explore practical use cases of cross-chain bridges in AIoT applications, including decentralized energy trading, healthcare data integration, and supply chain automation. This taxonomy provides a foundational framework for researchers and practitioners seeking to design secure and efficient cross-chain infrastructures in AIoT systems.
In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting.
In an uncertain world filled with cyberthreats, blockchain has proven to be a revolutionary technology of significant value to most industries. While blockchain is used extensively in the fields of energy, finance and governance, healthcare is among the key sectors whose applications have been most evident as far as its adoption into these sectors is concerned. Since data is currently regarded as both an asset and currency, security has emerged as a key issue especially in healthcare as more and more data breaches have emphasized the need for enhanced planning, requirements analysis and implementation of strong cybersecurity models. This paper introduces a cloud-based blockchain architecture The approach organizes network participants into clusters, with each cluster maintaining a single copy of the blockchain. This design introduces a new blockchain architecture tailored for secure healthcare data management, significantly lowering both computational demands and communication overhead particularly when compared with conventional Bitcoin networks and existing lightweight blockchain models, while at the same time, investigating how the proposed design adequately mitigates known security threats. Experimental results show that, with increasing number of nodes, the proposed model accelerates the updates of ledger, It achieves a 63% reduction in computational load while also decreasing network traffic by 10%.
Blockchain technology, also known as Distributed Ledger Technology (DLT), is a decentralized, distributed ledger that chains encrypted blocks of data together. Its inherent properties, such as transparency, data integrity, and security measures, make it a prominent technology for various industries. Consensus protocols, hashing, and smart contracts enhance its security and reliability. However, scalability has proven to be a significant challenge in implementing public blockchains. The rapid growth of network nodes and miners' transaction calculations are the main causes of scalability issues. Efficient data scalability is crucial, as it can result in reduced maintenance costs, improved user experience, and higher agility. In this paper, we explore methods to enhance the consensus algorithm and compare its performance with existing algorithms.
Andrea Michienzi, Laura Pollacci, Barbara Guidi, Francesco Maggio
Nowadays, Social Media represents an important window to address societal issues and promote social causes. However, Social Media suffer from several issues concerning fake news, misinformation, disinformation, etc. To address these issues, decentralization has been proposed to overcome current limitations. Blockchain-based Online Social Media (BOSM) offer verifiable platforms, usually enriched with reward systems that allow users to get paid according to the social value they create. Reward systems can economically empower creators and other individuals beyond high-quality content, allowing content creators to earn income. Considering the widespread use of BOSM platforms and various incentive methods, tools are needed to analyze and guide these rewarding strategies to avoid the risk of speculative mechanisms. In this paper, we propose BISON, a predictive and interpretable framework for identifying the drivers of success in blockchain-native articles. BISON can model success not as a purely financial outcome, but as a composite function of content attributes and user engagement patterns, as recorded on the blockchain. Its modular architecture allows for empirical validation across multiple datasets and makes it adaptable to other Web3 platforms. Additionally, our framework introduces Explainable AI into the blockchain content domain.