Smart contracts are commonly used for automated processing on blockchains, and research related to smart contracts is actively conducted. However, smart contracts are constrained by the fact that they cannot hold secret information due to transparency requirements, and thus smart contracts cannot perform calculations using secret information. In particular, they generally cannot generate digital signatures, which are computations using a secret key. This limits the potential of smart contracts. In this study, we propose a new scheme that enables smart contracts to generate signatures even if the smart contract does not hold a secret key. The proposed scheme allows smart contracts to securely delegate signature generation to off-chain servers that hold the signing key and do not support TEE. Even if the off-chain server is compromised by an attacker and the secret key (signing key) is stolen, the smart contract still generates a valid signature. We provide a new secure and effective signature generation approach using smart contracts and incentive mechanisms, even if the signing key is publicly available.
A polynomial commitment scheme (PCS) enables a prover to commit to a polynomial and later prove the correctness of its evaluation without revealing the polynomial. Although discrete logarithm-based PCSs offer succinct proofs, they are not quantum-safe. Lattice-based PCSs provide post-quantum security and additive homomorphism, making them suitable for applications such as zero-knowledge proofs and secure multiparty computation. In this article, we review two recent lattice-based PCSs, Greyhound and HyperWolf, both relying on the Module-SIS assumption but differing in target polynomial classes and proof techniques. In particular, Greyhound achieves a smaller proof size O(log log N) through folding and LaBRADOR proofs, while HyperWolf supports univariate and multilinear polynomials with lower verifier cost O(log N) using hypercube evaluation.
Nigeria faces an urgent energy challenge marked by chronic electricity shortages, dependence on fossil fuels, and worsening environmental degradation. This study examines the economic and environmental benefits of transitioning to renewable energy in Nigeria, adopting a mixed-methods approach that combines a systematic literature review, policy analysis, and synthesis of empirical case studies. Findings reveal that Nigeria possesses vast potential for solar, wind, biomass, and hydropower, capable of transforming its energy landscape. Economically, the adoption of renewable energy can generate employment, stimulate industrial growth, expand rural electrification, attract investment, and stabilize public finances by reducing vulnerability to global oil price shocks. Environmentally, it can reduce greenhouse gas emissions, improve air quality, conserve biodiversity, promote sustainable waste management, and enhance resilience to climate variability. Case studies demonstrate the effectiveness of decentralized solar mini-grids, biomass utilization, and hybrid systems in meeting local energy needs; however, persistent barriers, including weak policy enforcement, financing gaps, and infrastructural limitations remain. The study concludes that a comprehensive framework is required, built on policy alignment, financing innovation, institutional strengthening, infrastructure development, and social inclusion. Renewable energy transition thus represents not only a climate responsibility but also a strategic pathway for Nigeria’s sustainable economic and environmental future.
In response to the growing sophistication of cyber threats, traditional centralized authentication systems have become increasingly vulnerable, particularly to AI-driven attacks and large-scale system compromises. This paper proposes a novel blockchain-based authentication framework that integrates Byzantine Quorums (BQ) to enhance resilience, decentralization, and trust. The framework introduces a dynamic intersection-based verification mechanism, enabling accurate user authentication while continuously detecting and isolating malicious nodes. By leveraging the distributed and immutable nature of blockchain along with quorum-based consensus, the system significantly reduces the risks posed by Sybil, Eclipse, and double-spending attacks. Comparative analysis with existing frameworks demonstrates superior performance in terms of security, efficiency, and malicious node detection. The proposed solution holds strong potential for deployment in critical sectors such as finance, healthcare, and energy, where secure and scalable authentication is essential.
Edison A. Arteaga López, Gustavo A. Ramírez González, Carlos Alberto Astudillo
This paper proposes an ecosystem based on the Internet of Things (IoT) and integrated with Distributed Ledger Technologies (DLT), specifically IOTA, applied to the tourism sector with a focus on the hotel industry. In developing countries, particularly in Latin America, the hotel industry has been hindered by a lack of technological adoption, which has limited its growth and competitiveness. This paper presents a methodology to enhance the interoperability, security, and traceability of tourism data through the integration of IoT and DLT. The study explores a novel technological architecture, presents preliminary results from a prototype implementation, and outlines future research directions.
IoT devices constitute an important component of Industry 4.0 paradigm, but are greatly hindered by their inherent resource constraints. Resource sharing is therefore an essential operating requirement for these devices but lack of privacy and heavy reliance on centralized architectures pose a serious risk for stable functioning. Use of decentralized and high availability platforms like distributed ledgers can provide divergent and distributed networking conditions but leaves any inter-device interactions completely exposed to third party view. To resolve this, we utilize an innovative combination of smart contracts alongside a zero-knowledge proof generator, known as Tornado Cash, to align IoT devices on a distributed resource exchange platform with privacy guarantees. In concert with public-key cryptography, our solution provides a framework for resource constrained IoT machines to interact through the blockchain for resource exchange purposes with strong privacy guarantees for both devices. Absolute anonymity is ensured by the protocol’s inherent architecture, meaning that participant devices do not reveal any sensitive information and consequently it becomes nontrivial to breach the privacy of either participant. Performance evaluations performed by testing ZeKSA against a competing & comparatively vulnerable protocol yield promising outcomes in terms of blockchain metrics like gas usage & incurred transaction costs.
Anthony Uchenna Eneh, Love Allen Chijioke Ahakonye, Jae Min Lee, D. Kim
Insurance is essential for financial resilience; however, traditional systems remain costly, opaque, and inaccessible, particularly in underserved regions. In many such communities, informal savings schemes like akawo, esusu, and ajo offer grassroots risk pooling; however, these schemes lack scalability, transparency, and fraud resistance. Existing blockchain-based insurance platforms address some of these issues, but often replicate centralized models, rely on token-based governance, or overlook the cultural relevance of local financial systems. This paper presents PureAjo, a fully decentralized peer-to-peer insurance platform that digitizes traditional communal models using smart contracts. Users connect their wallets to join insurance networks, pay premiums, file claims, and vote on outcomes, entirely from the frontend using wallet signatures. Governance follows a one-wallet, one-vote model, eliminating the need for tokens or custodial logic. We evaluated PureAjo on the Mumbai testnet. Core contract interactions executed with average gas usage between 88k and 143k units, and confirmed within 10–14 seconds. PureAjo demonstrates that fully decentralized, culturally grounded insurance systems are not only possible but also performant. It lays the groundwork for scalable deployment on PureChain, a dedicated Layer 2 network optimized for mutual finance.
Blockchain technology enables semi-anonymous transactions, where user identities are not directly revealed but instead linked to cryptographic wallet addresses. While this design enhances privacy and security, the intrinsic transparency of public blockchains raises concerns about the true anonymity of users. To address this, privacy-enhancing techniques such as CoinJoin were developed to obscure transaction flows. CoinJoin is a Bitcoin-based mixing technique that combines multiple inputs and outputs into a single larger transaction, making it difficult to trace the original senders and recipients. Although CoinJoin was intended to support privacy-preserving transactions, it is often exploited for money laundering. As a result, there is a growing need to identify CoinJoin transactions in order to prevent such misuse. This research aims to detect unidentified CoinJoin transactions on the Bitcoin blockchain using a machine learning approach. Unlike prior work that relied on heuristics or traditional machine learning algorithms, we propose an improved methodology relying on random forest ($\mathbf{R F}$) models designed to handle imbalanced datasets. Specifically, we introduce a novel variant, the biased random forest (BRAF), aimed at improving detection performance under significant class imbalance.
The extreme and often seemingly irrational pricing of non-fungible tokens (NFTs) has inspired interest in identifying their valuation determinants. While visual features have been proposed as partial determinants for NFT price variation, previous studies often rely on non-interpretable models and fail to control for a critical confounder: NFT collection identity. In this paper, we analyze over 160,000 NFTs across 32 major collections to evaluate the explanatory power of visual features for NFT pricing. We show that visual models achieve high predictive performance only when collection label leakage is present, falsely inflating results by learning collection-specific price tiers. Once collection identity is controlled, we find that visual featuresinterpretable or deep-learning based-offer limited generalizability and weak predictive power across collections. However, for a subset of collections, visual features do exhibit modest withincollection predictability, a phenomenon we term photorelevance. We introduce a framework to quantify photorelevance and identify two collection-level characteristics-image contrast variance and price variance-as statistically significant predictors of it. Our findings highlight the methodological importance of addressing collection label leakage and suggest that aesthetic attributes play a limited and only localized role in NFT valuation. Code and data are publicly released to enable reproducibility and further research.
Lana AL-Khalaileh, Tareq Al-Billeh, Abdul Salam Al-Findi, Odai Al-Hailat
This study deals with a new technology in contracting, resulting from the information technology (IT) revolution in the field of electronic transactions, which is called “smart contracts”. The latter has constituted a breakthrough in the field of contracting since it provides automation, which underlies many advantages for contractors, so that the software works of smart contracts provide immediate and automatic execution of the contract, which provides speed of implementation and security from manipulation after concluding the contract. So, it provides elements of technical security and trust for this type of contract. This new contractual pattern is considered one of the first in the provisions of Islamic Sharia, which urges us to know the extent of its compatibility with its contracting system. The study concluded with several recommendations, the most significant being that international accords lack comprehensive legislation governing transactions executed through smart contracts. While they contain certain restrictions about contracts formed through contemporary electronic methods, they inadequately elucidate the characteristics of such contracts and examine their specifics. The legal issues associated with smart contracts stem from their connection to digital currency, which is banned by Sharia law.
Giovanni Ciaramella, Fabio Martinelli, Francesco Mercaldo, Paolo Mori · 5 authors
Blockchain adoption has significantly expanded in recent years, with the emergence of smart contracts facilitating practical applications in many domains. Since smart contracts can execute cryptocurrency transfers, malicious users have started implementing fraudulent smart contracts to deceive blockchain users and steal their funds. To mitigate this issue, this paper proposes an approach to detect fraudulent smart contracts leveraging Federated Machine Learning. Our approach generates an image representation for each smart contract by extracting the opcodes and assigning a unique RGB pixel. We utilized two publicly available datasets, including malicious and trustworthy smart contracts, to train multiple models on non-independent and Identically Distributed data to better represent a real-world scenario, achieving interesting results in accuracy. To the best of our knowledge, this article represents the first approach in the unique identification of fraudulent smart contracts using opcodes with a specific color, also leveraging a Federated Machine Learning approach in the blockchain environment.
With the growing adoption of blockchain interoperability, cross-chain token transfers have become a critical aspect of decentralized finance and blockchain ecosystems. Polkadot, a blockchain network designed to facilitate interoperability, employs a unique cross-chain message passing (XCMP) protocol to enable token transfers among the relay chain and parachains. However, token transfer time within this system, particularly the relationship between its average and standard deviation and underlying factors, has not been extensively investigated. In this paper, we conduct an empirical analysis of token transfer times in Polkadot and Kusama mainnets using ParaSpell to measure XCMP delays across various cross-chain messaging mechanisms, including Downward and Upward Message Passings and Horizontal Relay-routed Message Passing. Our findings reveal a significant correlation between the mean and standard deviation of token transfer times, suggesting the presence of fundamental systemic delays. Furthermore, we propose a theoretical model based on validator-to-validator broadcast time, which appears to be the dominant factor influencing transfer latency. Index Terms-Cross-chain message passing, Polkadot, Token transfer time, Experiment, Theoretical analysis
Andrew Le Gear, Farshad Ghassemi Toosi, Ashish Rajendra Sai, Tawny Whatmore · 5 authors
In the unregulated world of Initial Coin Offerings (ICOs), hiding malicious trading is all too easy in a large-scale set of transactions. This paper uses a graph-based representation of the blockchain to identify a topology that reveals suspicious intent to manipulate the perceived value of those offerings. As the computational complexity of identifying this topology could be prohibitive for unfiltered data-sets, this work derives metrics indicative of the topology. Using these explicitly-defined metrics and a past degradation of service on the Ethereum network originating with the iFishYunYu token, we show how this approach can reveal it to have been a deliberate attack, rather than simply an unprecedentedly highly-traded token. The formalization of this approach in the paper will allow detection of other such “pump-and-dump” attacks in the future.
Smart contracts have emerged as key components within decentralized environments, enabling the automation of transactions through self-executing programs. While these innovations offer significant advantages, they also present potential drawbacks if the smart contract code is not carefully designed and implemented. This paper investigates the capability of large language models (LLMs) to detect OWASP-inspired vulnerabilities in smart contracts beyond the Ethereum Virtual Machine (EVM) ecosystem, focusing specifically on Solana and Algorand. Given the lack of labeled datasets for non-EVM platforms, we design a synthetic dataset of annotated smart contract snippets in Rust (for Solana) and PyTeal (for Algorand), structured around a vulnerability taxonomy derived from OWASP. We evaluate LLMs under three configurations: prompt engineering, fine-tuning, and a hybrid of both, comparing their performance on different vulnerability categories. Experimental results show that prompt engineering achieves general robustness, while fine-tuning improves precision and recall on less semantically rich languages such as TEAL. Additionally, we analyze how the architectural differences of Solana and Algorand influence the manifestation and detectability of vulnerabilities, offering platform-specific mappings that highlight limitations in existing security tooling. Our findings suggest that LLM-based approaches are viable for static vulnerability detection in smart contracts, provided domain-specific data and categorization are integrated into training pipelines.
Cross chain interoperability in blockchain systems exposes a fundamental tension between user privacy and regulatory accountability. Existing solutions enforce an all or nothing choice between full anonymity and mandatory identity disclosure, which limits adoption in regulated financial settings. We present VeilAudit, a cross chain auditing framework that introduces Auditor Only Linkability, which allows auditors to link transaction behaviors that originate from the same anonymous entity without learning its identity. VeilAudit achieves this with a user generated Linkable Audit Tag that embeds a zero knowledge proof to attest to its validity without exposing the user master wallet address, and with a special ciphertext that only designated auditors can test for linkage. To balance privacy and compliance, VeilAudit also supports threshold gated identity revelation under due process. VeilAudit further provides a mechanism for building reputation in pseudonymous environments, which enables applications such as cross chain credit scoring based on verifiable behavioral history. We formalize the security guarantees and develop a prototype that spans multiple EVM chains. Our evaluation shows that the framework is practical for today multichain environments.
Maneesha Papireddygari, Xintong Wang, Bo Waggoner, David M. Pennock
Automated Market Makers (AMMs) are used to provide liquidity for combinatorial prediction markets that would otherwise be too thinly traded. They offer both buy and sell prices for any of the doubly exponential many possible securities that the market can offer. The problem of setting those prices is known to be #P-hard for the original and most well-known AMM, the logarithmic market scoring rule (LMSR) market maker [Chen et al., 2008]. We focus on another natural AMM, the Constant Log Utility Market Maker (CLUM). Unlike LMSR, whose worst-case loss bound grows with the number of outcomes, CLUM has constant worst-case loss, allowing the market to add outcomes on the fly and even operate over countably infinite many outcomes, among other features. Simpler versions of CLUM underpin several Decentralized Finance (DeFi) mechanisms including the Uniswap protocol that handles billions of dollars of cryptocurrency trades daily. We first establish the computational complexity of the problem: we prove that pricing securities is #P-hard for CLUM, via a reduction from the model counting 2-SAT problem. In order to make CLUM more practically viable, we propose an approximation algorithm for pricing securities that works with high probability. This algorithm assumes access to an oracle capable of determining the maximum shares purchased of any one outcome and the total number of outcomes that has that maximum amount purchased. We then show that this oracle can be implemented in polynomial time when restricted to interval securities, which are used in designing financial options.
The generation and exchange of diverse e-health records, such as Personal Health Records (PHRs) and Electronic Medical Records (EMRs), have become increasingly critical in supporting comprehensive clinical decision-making across healthcare institutions. While significant progress has been made in securely and efficiently sharing these records, current solutions often struggle to handle multiple types of e-health data simultaneously. Moreover, a patient-centric approach, which balances ease of use for patients with the need to ensure their privacy, remains a critical challenge that requires further exploration. In this paper, we propose a decentralized, IoT-enabled e-health data-sharing model leveraging blockchain and cloud technologies, designed to support both PHRs and EMRs. Our model incorporates advanced security features, including zero-knowledge proof, elliptic-curve cryptography, and decentralized access control, to ensure a practical, secure, and privacy-preserving system. We simulate a real healthcare environment to demonstrate its practical feasibility, and performance evaluations show our system’s superior efficiency and enhanced security compared to existing solutions.
Blockchain holds promise for reshaping insurance operations by enhancing transparency, automation, and trust. However, existing blockchain-based insurance prototypes often face limitations in transaction speed, cost efficiency, and scalability. This pilot study investigates a decentralised insurance platform implemented on the Algorand network, aiming to address these challenges. We focus on parametric insurance for flight delays, leveraging smart contracts and oracles to manage policy issuance, coverage activation, and claims. We conduct both sequential and stochastic simulations to evaluate performance under controlled and realistic transaction patterns. Our results show that average confirmation times drop from tens or hundreds of milliseconds, as seen in previous systems, to as low as 20 ms in sequential tests and approximately 6.60 ms in stochastic scenarios. Moreover, transaction fees remain minimal, improving cost-effectiveness by approx. $99.53 \%$ for claim operations compared to existing studies. The system sustains an average throughput of 44.44 TPS, with faster policy issuance and claims processing than comparable Ethereum-based solutions. These findings suggest that Algorand’s Pure Proof-of-Stake consensus and our architectural approach significantly enhance operational efficiency, supporting the feasibility of largescale decentralised insurance services. While scaling the experiment, exploring complex policies, and refining DAO governance structures are needed, this work provides a solid foundation for real-world adoption. This research supports blockchain-driven transformation in insurance markets and can serve as a step forward in Insurance 4.0.
Online review systems play a critical role in shaping consumer decisions and business reputation. However, they are increasingly vulnerable to manipulation, particularly through a coordinated practice known as review bombing. This occurs when large numbers of negative reviews are posted in a short time frame, often driven by political or ideological motives rather than genuine user experience. Such attacks distort public perception and can cause significant economic harm, particularly to small and medium-sized businesses. This paper presents a decentralized review platform architecture designed to enhance fairness, transparency, and resistance to manipulation. The system leverages Blockchain (BC) technology to enforce a one-NFT-one-review policy, in which each review is linked to a unique Non-Fungible Token (NFT) representing the user’s right to post. To ensure real-world authenticity, the platform incorporates a Proof-of-Visit mechanism using timelimited QR codes displayed on-site. Only users who scan the QR code at a physical location are authorized to mint the NFT. A prototype was implemented using CosmJS, CW721 smart contracts, and Keplr wallet integration, and deployed on the Neutron testnet. Empirical evaluation shows that the total cost for a complete review submission-including NFT minting and review posting-is approximately 0.012845 NTRN ($\approx$ ${\$}$ 0.00257${\$}$ USD), confirming the system’s economic feasibility. By addressing both the social dynamics of review bombing and the technical limitations of centralized platforms, this study proposes a scalable and cost-effective BC-based architecture for secure, verifiable, and tamper-resistant online review systems.
Blockchain transaction verification is fundamental to decentralized financial applications, ensuring both transaction authorization and integrity. Most existing verification schemes utilize an equal weight model that grants identical rights to all participants. However, these schemes fail to capture real-world scenarios like Proof-of-Stake blockchains, where participants have right discrepancies. Additionally, many schemes depend on trusted third parties for key generation, introducing single points of failure and compromising security. To address these limitations, we propose WBlock that is a weighted and decentralized verification scheme. WBlock involves a distributed key generation protocol that embeds each entity's weight into its secret share, eliminating the need for trusted third parties. It further employs a Schnorr-type weighted threshold signing protocol, enabling distributed transaction authorization while reflecting participant weight in signature shares. Security analysis shows that WBlock achieves both correctness and unforgeability. Comparative theoretical analysis shows that our key generation and signing protocols outperform existing methods in terms of round complexity, communication overhead and data size. Experimental results confirm that WBlock achieves a balance between security and efficiency, with acceptable overhead for real-world blockchain deployment.
Tanmay Thapliyal, Aman Gupta, Rachit Agarwal, Sandeep K. Shukla
The emergence of Blockchain 2.0, along with the introduction of smart contracts (SCs), has facilitated the development of automated decentralized financial interactions on various platforms. These programmable contracts are utilized in applications including decentralized finance (DeFi), token issuance, and automated fund transfers. However, the pseudonymous nature of blockchain transactions, combined with automation capabilities and mixing services, has been exploited by malicious actors to launder illicit proceeds. Most of the techniques in the state-of-theart approaches detect addresses related to such illicit actors by relying on machine learning techniques that use only transactionbased features. In this work, we propose an algorithm to detect and identify addresses that are related to scamming activities, such as phishing. We focus on Ethereum, one of the widely adopted blockchains, and analyze over 1.8 billion transactions to identify SC deployments, thereby creating an SC deployment mapping. Alongside this mapping, we construct a transaction graph of known scam-related accounts, which we use to identify accounts involved in laundering proceeds from these scams. This approach identifies $\mathbf{3 2, 2 7 2}$ accounts associated with known scam addresses. The proposed algorithm takes an average of 0.024 seconds to determine whether an address is illicit. We also identify three recurring motifs characteristic of scam-related addresses, which can aid blockchain forensic frameworks in detecting such activities. By validating our findings with crypto-forensic tools, we uncover additional malicious addresses and provide actionable insights for law enforcement agencies.
This paper presents a quantitative model to estimate the sustainability of blockchain networks. Blockchains with three different consensus protocols such as Proof of Work (PoW), Proof of Stake (PoS) and Proof of Hybrid consensus protocols across PoW and PoS(PoH) [12], are considered in this research. Sustainability issues in blockchain network systems due to their energy-depleting mining processes and transactions are hindering the technology from further advancement. The sustainability of concern in blockchain networks is assumed, in this paper, to be an inverse of their energy consumption without loss of generality and intuitiveness. The novelty of the proposed model is that the sustainability is estimated quantitatively in a specific context of blockchain network architectures and stochastic behaviors of the transactions. An extensive variety of variables are employed to build a model that is blockchain (e.g., $\mathrm{PoW}, \mathrm{PoS}$ and PoH)-specific, as three representative benchmark architectures with respect to consensus protocols, and each of those will be quantitatively expressed via the baseline chain model [7], the PoS chain model [9] and the PoH chain model [12], respectively. Extensive numerical simulations will be conducted to demonstrate various design considerations to be taken in specific regard of sustainability versus a few primary design variables as employed in the sustainability model. The proposed sustainability model is expected to establish a sound and quantitative foundation for the design of sustainable blockchain networks.
Purpose This study explores Bitcoin’s infectious narrative through the framework of epidemiological models, specifically the Susceptible-Infected-Recovered (SIR) model with constant force of infection. Design/methodology/approach The SIR model, which is traditionally used for infectious diseases, categorizes Bitcoin wallets into three groups: susceptible (open wallets), infected (active wallets), and recovered (inactive wallets). The analysis uses monthly data from January 2011 to December 2022 to examine two significant Bitcoin price bubbles. Findings The study reveals distinct dynamics between the bubbles by incorporating time-dependent infection (β) and recovery (γ) rates. During the 2017–18 bubble, the infection spread was slower, characterized by a lower β value of 0.17 and a prolonged recovery process with a γ Value of 0.01. On the contrary, the 2020–22 bubble saw a rapid infection rate, with a β value of 0.8 and a faster recovery rate γ of 0.07. In the end, Bitcoin has a high infection rate, spreading almost as rapidly as measles or whooping cough. Originality/value The study introduces novel insights into explaining the Bitcoin price bubbles using epidemiological models. Like these diseases, Bitcoin also spreads quickly and aggressively within an exposed population (risk loving investors). Meanwhile, recovery rate shows similarities to diphtheria and tuberculosis. These diseases have prolonged infection periods and take a long time to cure. In terms of geographical distribution, Bitcoin exhibits pandemic features due to its global presence.