Abhishek Bhattarai, Abdulhadi Sahin, Maryna Veksler, Ahmet Kurt · 7 authors
As cryptocurrencies have become increasingly used as an alternative to regular cash and credit card payments, the wallet solutions/apps that facilitate their use have also become increasingly popular. This has also intensified the involvement of these crypto wallet apps in criminal activities such as ransom requests, money laundering, and transactions on dark markets. From a digital forensics point of view, it is crucial to have tools and reliable approaches to detect these wallets on devices and extract their artifacts quickly with greater efficiency. However, with current research and trends, forensic investigators still need to manually extract these file artifacts, which delays the time-sensitive investigation findings. As mobile devices increasingly facilitate cryptocurrency transactions, there emerges a critical gap and need for automated evidence extraction to detect crucial artifacts preventing illicit activities. Therefore, in this paper, we present a comprehensive framework that incorporates various machine learning (ML), image processing, and natural language processing (NLP) approaches to enable fast and automated extraction/triage of crypto-related artifacts from Android and iOS devices. Specifically, our method can automatically detect which crypto wallet exists on the device, their artifacts (i.e., database/log files), along with the crypto-related images, web browsing data, and SMS conversations. For each type of data, we offer a specific ML technique, such as Support Vector Machine, Logistic Regression, and Neural Networks, to detect and classify these files. Our evaluation results show very high accuracy compared to alternative tools: our wallet classification model achieves 91% recall, crypto-related image classification achieves 75% accuracy, browsing data achieves 100% accuracy, and the SMS message model achieves 85% accuracy.
Figueiredo, Bernardo J. R., Ferreira, Marco P. M., Matos, João, Cova, Marco
A new approach to asset management and traceability emerges upon the integration of Non-Fungible Tokens (NFTs) and Digital Twin (DT) technology. While NFTs are widely used in digital art and gaming, their potential for securing real-world assets in DT simulations remains under-explored. A generic NFT-driven DT simulation platform could transform asset management by enhancing traceability, optimizing operations, and fostering sustainability within and across industries. In livestock management, DTs can model individual animals in real time, capturing data on health and growth. Linking this data to NFTs ensures ownership and provenance, improving traceability and accountability. Similarly, in manufacturing, DTs can identify inefficiencies, reducing waste and energy use. It is pressing to improve decision-making and operational efficiencies throughout distinct contexts. A Systematic Literature Review (SLR) was conducted following PRISMA. The goal was to build a solid, unbiased foundation for our research, contribute lasting value to the community, and identify where our work can make the most impact. From an initial set of 114 papers, the authors screened and classified the most relevant. This led to a final selection of 8 papers for full-text reading and in-depth analysis. They are presented in detail and compared in order to depict the current state of the art in the field. Results reveal a significant gap concerning the topic, particularly highlighting the absence of simulation environments that align with the previous proposal presented by the authors: a comprehensive NFT-driven and DT simulation platform that transforms NFT-based asset management from static ownership records to dynamic, provides simulation operational tools, enables real-time monitoring, predictive maintenance, and performance optimization for real-world assets.
Rocsana Bucea-Manea-Țoniş, Andrei Gabriel Antonescu, Constanța Mihăilă
Blockchain technology is reshaping the sports industry by enhancing transparency, data security, and fan engagement through applications such as smart contracts, tokenized sponsorships, and decentralized ticketing. This study investigates blockchain adoption in Romanian team sports, specifically football and basketball, through a comparative analysis based on a survey of 293 sports professionals (213 from football and 80 from basketball). Using structural equation modeling (SEM) with SmartPLS and cluster analysis in SPSS, the study explores the perceived benefits of blockchain and its relationship with athlete performance. The findings reveal distinct adoption patterns: football shows higher use of blockchain in ticketing and fan engagement, while basketball leads in performance analytics and financial support mechanisms. Statistically significant differences were confirmed through MANOVA, and clustering revealed varied stakeholder perceptions across professional roles. Benchmarking against sectors like finance and healthcare highlights transferable best practices for blockchain integration in sports.
Blockchain-based smart contracts offer a new take on credible commitment, where players can commit to actions in reaction to actions of others. Such reaction-function games extend on strategic games with players choosing reaction functions instead of strategies. We formalize a solution concept in terms of fixed points for such games, akin to Nash equilibrium, and prove equilibrium existence. Reaction functions can mimic "trigger" strategies from folk theorems on infinitely repeated games -- but now in a one-shot setting -- for instance to support Pareto-improvements on Nash equilibrium outcomes. In some games, this can even be done through risk-free, safe reaction functions. We apply our theoretical framework to symmetric investment games, which includes two prominent classes of games, namely weakest-link and public-good games. In both cases, we highlight a particular safe and optimal reaction function. In this way, our findings highlight how blockchain-based commitment can help overcome trust and free-riding barriers.
The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing criminality in the cryptocurrency realm.
The distribution of consensus power is a cornerstone of decentralisation, influencing the security, resilience, and fairness of blockchain networks while ensuring equitable impact among participants. This study provides a rigorous evaluation of consensus power inequality across five prominent blockchain networks - Bitcoin, Ethereum, Cardano, Hedera, and Algorand - using data collected from January 2022 to July 2024. Leveraging established economic metrics, including the Gini coefficient and Theil index, the research quantitatively assesses how power is distributed among blockchain network participants. A robust dataset, capturing network-specific characteristics such as mining pools, staking patterns, and consensus nodes, forms the foundation of the analysis, enabling meaningful comparisons across diverse architectures. Through an in-depth comparative study, the paper identifies key disparities in consensus power distribution. Hedera and Bitcoin demonstrate more balanced power distribution, aligning closely with the principles of decentralisation. Ethereum and Cardano demonstrate moderate levels of inequality. However, contrary to expectations, Ethereum has become more concentrated following its transition to Proof-of-Stake. Meanwhile, Algorand shows a pronounced centralisation of power. Moreover, the findings highlight the structural and operational drivers of inequality, including economic barriers, governance models, and network effects, offering actionable insights for more equitable network design. This study establishes a methodological framework for evaluating blockchain consensus power inequality, emphasising the importance of targeted strategies to ensure fairer power distribution and enhancing the sustainability of decentralised systems. Future research will build on these findings by integrating additional metrics and examining the influence of emerging consensus mechanisms.
Abdulrahman Alzahrani, Amin Y. Noaman, Ahmed A. A. Gad-Elrab, Fathy Eassa · 7 authors
Interoperability among heterogeneous blockchain platforms remains a significant challenge in decentralized ecosystems, especially with the growing adoption of blockchain across various industries. This research introduces the Data Standardization Module (DSM). This framework facilitates seamless communication between platforms like Ethereum and Hyperledger Fabric using a unified schema for data transformation and exchange. DSM leverages Concise Binary Object Representation (CBOR) encoding, which reduces data size by 60 % and achieves a compression ratio (CR) of 2.5. This outperforms conventional JSON-based methods, typically achieving only 20–30 % compression with lower CR values. The system ensures data security and integrity through end-to-end encryption, access control, and validation mechanisms. Performance evaluations show that DSM supports an average throughput of 250 transactions per second (TPS). In comparison, interoperability frameworks like Cosmos and Polkadot typically achieve 100–150 TPS, while early implementations of Hyperledger Fabric reported TPS as low as 300. However, optimized settings of Hyperledger Fabric now achieve over 100,000 TPS. DSM strikes a balance by providing high throughput with minimal resource overhead, making it suitable for real-world applications. This work advances blockchain interoperability by offering a lightweight, secure, and scalable framework, ideal for high-frequency use cases in healthcare, finance, and supply chains.
Background: With the enhanced data amount being created, it is significant to various organizations and their processing, and managing big data becomes a significant challenge for the managers of the data. The development of inexpensive and new computing systems and cloud computing sectors gave qualified industries to gather and retrieve the data very precisely however securely delivering data across the network with fewer overheads is a demanding work. In the decentralized framework, the big data sharing puts a burden on the internal nodes among the receiver and sender and also creates the congestion in network. The internal nodes that exist to redirect information may have inadequate buffer ability to momentarily take the information and again deliver it to the upcoming nodes that may create the occasional fault in the transmission of data and defeat frequently. Hence, the next node selection to deliver the data is tiresome work, thereby resulting in an enhancement in the total receiving period to allocate the information. Methods: multi-node data repetition. Blockchain is involved in offering a transparency to the application of transmission. A simultaneous multi-threading framework confirms quick data channeling to various network receivers in a very short time. Therefore, an advanced method to securely store and transfer the big data in a timely manner is developed in this work. A deep learning-based smart contract is initially designed. The dilated weighted recurrent neural network (DW-RNN) is used to design the smart contract for the Ethereum blockchain. With the aid of the DW-RNN model, the authentication of the user is verified before accessing the data in the Ethereum blockchain. If the authentication of the user is verified, then the smart contracts are assigned to the authorized user. The model uses elliptic Curve ElGamal cryptography (EC-EC), which is a combination of elliptic curve cryptography (ECC) and ElGamal encryption for better security, to make sure that big data transfers on the Ethereum blockchain are safe. The modified Al-Biruni earth radius search optimization (MBERSO) algorithm is used to make the best keys for this EC-EC encryption scheme. This algorithm manages keys efficiently and securely, which improves data security during blockchain operations. Results: smart contracts.
This study proposes an AI-assisted governance framework to enhance decision-making within decentralized autonomous organizations (DAOs). By integrating chain-of-thought (CoT) reasoning with stakeholder-adaptive recommendations, the framework improves decision alignment, increases voter participation, and enhances governance transparency. Through simulations based on historical KlimaDAO data, the system achieved a 97% alignment with past decisions, a projected 40% increase in participation, and a 35% improvement in governance clarity. To support quantitative analysis in tokenomics, we developed a tailored CoT reasoning strategy, effectively reducing information asymmetry and generating structured, trustworthy recommendations. These results underscore the potential of AI to foster more inclusive and transparent DAO governance. Future work will explore deploying lightweight AI models and extending this approach to a broader range of DAO ecosystems.
Bitcoin is a representative decentralized currency system. For the security of Bitcoin, fairness in the distribution of mining rewards plays a crucial role in preventing the concentration of computational power in a few miners. Here, fairness refers to the distribution of block rewards in proportion to contributed computational resources. If miners with greater computational resources receive disproportionately higher rewards, i.e., if the Rich Get Richer (TRGR) phenomenon holds in Bitcoin, it indicates a threat to the system's decentralization. This study analyzes TRGR in Bitcoin by focusing on unintentional blockchain forks, an inherent phenomenon in Bitcoin. Previous research has failed to provide generalizable insights due to the low precision of their analytical methods. In contrast, we avoid this problem by adopting a method whose analytical precision has been empirically validated. The primary contribution of this work is a theoretical analysis that clearly demonstrates TRGR in Bitcoin under the assumption of fixed block propagation delays between different miners. More specifically, we show that the mining profit rate depends linearly on the proportion of hashrate. Furthermore, we examine the robustness of this result from multiple perspectives in scenarios where block propagation delays between different miners are not necessarily fixed.
Aggelos Kiayias, Elias Koutsoupias, Giorgos Panagiotakos, Kyriaki Zioga
Blockchain transactions consume diverse resources, foremost among them storage, but also computation, communication, and others. Efficiently charging for these resources is crucial for effective system resource allocation and long-term economic viability. The prevailing approach, one-dimensional pricing, sets a single price for a linear combination of resources. However, this often leads to under-utilization when resource capacities are limited. Multi-dimensional pricing, which independently prices each resource, offers an alternative but presents challenges in price discovery. This work focuses on the welfare achieved by these two schemes. We prove that multi-dimensional pricing is superior under stable blockchain conditions. Conversely, we show that one-dimensional pricing outperforms its multi-dimensional counterpart in transient states, exhibiting faster convergence and greater computational tractability. These results highlight a critical trade-off: while multi-dimensional pricing offers efficiency gains at equilibrium, its implementation incurs costs associated with system transitions. Our findings underscore the necessity for a deeper understanding of these transient effects before widespread adoption. Finally, we propose mechanisms that aim to mitigate some of these issues, paving the way for future research.
Tianjiao Wang, Abdullah Al Mamun, Mohammad Masukujjaman, Qing Yang
The growing complexity and vulnerability of global supply chains underscore the need for robust frameworks to enhance resilience and sustainability. This quantitative study investigates the enablers of supply chain resilience and examines its dual role as both a direct contributor to sustainability performance and a mediator in the relationship between blockchain technology and sustainability outcomes. A cross-sectional approach was used to collect 387 valid responses from medium- and large-scale logistics firms operating in four major logistics hub cities in China. The data were analyzed using a structural equation modeling approach by applying partial least squares structural equation modeling. The results show that the proposed model explains 37.4% of the variance in SCR (R² = 0.374), 25.2% in economic performance (R² = 0.252), and 30.3% in environmental performance (R² = 0.303). Among the blockchain capabilities, transparency (β = 0.264, p = 0.001) and transaction cost efficiency (β = 0.212, p = 0.000) had the strongest direct effects on SCR, while value co-creation (β = 0.371, p = 0.000) significantly mediated the path between SCR and sustainability outcomes. Furthermore, multi-group analysis revealed significant differences based on firm size and operational tenure, such as a stronger effect of value chain integration on SCR among firms operating for eight years or less (β = 0.421, p = 0.022). Theoretically, this study integrates the multidimensional dynamic capabilities perspective with transaction cost economics, providing a nuanced understanding of blockchain’s role in enhancing supply chain resilience and sustainability, and enriching dynamic capability theory. Practically, it offers actionable insights for practitioners and policymakers by identifying enablers and barriers to blockchain adoption and emphasizing the need for standardized frameworks and supportive policies. This study advances the understanding of blockchain’s transformative potential for building resilient, adaptive, and sustainable supply chains amid global volatility.
Abstract The adoption of blockchain technology in supply chain management has gained significant attention due to its potential to enhance transparency, traceability, and accountability. However, successful blockchain implementation requires careful consideration of governance, regulatory, and ethical dimensions. Through a narrative literature review, this paper examines how blockchain can be effectively leveraged in sustainable supply chains, focussing on governance challenges and policy considerations. The review reveals significant gaps in current understanding of blockchain governance, including a lack of standardisation, limited integration between technical and institutional aspects, and insufficient attention to ethical implications. The analysis provides a comprehensive examination of the regulatory landscape, identifying critical areas requiring policy attention and suggesting pathways for implementation. The paper contributes to the literature by synthesising the current understanding of governance challenges and proposing policy directions for addressing these gaps. As blockchain technology continues to evolve, the paper emphasises the need for balanced governance approaches that promote innovation while ensuring responsible implementation across supply chain ecosystems.
<p><em>This study aims to analyse the influence of the degree of fiscal decentralization, balancing funds, and economic growth on the Islamic Human Development Index (I-HDI) in eight provinces in Indonesia with the lowest I-HDI scores. To fill the research gap, these provinces were categorised based on I-HDI intervals. This study contributes in addressing empirical gaps by focusing on the regions that have received limited attention in the context of Islam-based human development, as well as substantive gaps by integrating variables of fiscal decentralization and balancing funds that have rarely been directly examined in relation to the I-HDI. Methodologically, the System Generalised Method of Moments (System-GMM) approach is applied to address potential endogeneity and the dynamic nature of panel data. A region’s capacity to finance public expenditure is reflected in the degree of fiscal decentralization, while balancing funds serves as the main source for financing public welfare. Although economic growth is generally expected in enhancing welfare, it may exacerbate social inequality if not accompanied by inclusive policies and equitable distribution. The results of this study indicate that the degree of fiscal decentralization has no significant effect on I-HDI in either the short or long term. Conversely, balancing funds has a significantly negative impact, reflecting allocative inefficiency. Economic growth also negatively affects the I-HDI across both time horizons, indicating that its benefits are not evenly distributed. These findings emphasise the need for comprehensive policy evaluation to improve resource allocation efficiency and promote equitable and sustainable human development in line with the principles of Maqasid al-Shariah (Sharia principles).</em></p>
This study investigates the implications of Central Bank Digital Currency (CBDC) implementation and fintech adoption on the effectiveness of monetary policy, emphasizing the mediating role of financial system stability and the moderating influence of public trust in central banks. The research addresses a pressing issue in the digital transformation of global finance: whether digital currencies issued by central banks can enhance policy responsiveness in increasingly cashless and decentralized economies. Using an exploratory qualitative method, this study integrates a systematic review of post 2020 academic literature and central bank reports from The Bahamas, Nigeria, and China. A conceptual framework is developed to examine causal relationships among CBDC design, fintech integration, institutional trust, and policy effectiveness. The findings reveal that CBDC impact is highly context dependent; programmable and inclusive designs, such as China’s Digital Yuan, significantly enhance monetary transmission, whereas technical and social barriers, such as in Nigeria, limit policy effectiveness. The Bahamas serves as an intermediate case where offline and identity linked digital currency supports inclusion and moderate policy gains. The analysis confirms that financial stability mediates the relationship between digital innovation and policy outcomes, while public trust either strengthens or diminishes policy reach. This research contributes to the understanding of CBDC as a policy tool by highlighting institutional, technological, and behavioral factors that determine its success. Implications suggest that policymakers must adopt a multidimensional approach that combines digital infrastructure readiness with strong governance and trust building measures.
Hamdan Bukenya Ntare, John Weirstrass Muteba Mwamba, Franck Adékambi
There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and diversification benefits of integrating cryptocurrencies into South African bank equity portfolios. Using advanced financial engineering techniques, including multi-asset particle swarm optimizer (MA-PSO), random optimizer, and a static equal-weighted portfolio (EWP) model, this study analyzed the dynamic portfolio performance and diversification of cryptocurrencies in the 2017–2024 period. The portfolio performance of the three methods is also compared with the results from the traditional one-period mean–variance optimization (MVO) method. The findings underscore the superiority of dynamic models over static EWP in assessing the impact of cryptocurrency inclusion in bank equity portfolios. While pre-COVID-19 studies identified cryptocurrencies as effective hedges against market downturns, this protective role appears attenuated in the post-COVID-19 era. The dynamic MA-PSO model emerges as the optimal approach, delivering better-diversified portfolios. Consequently, South African portfolio managers must carefully evaluate investor risk tolerance before incorporating cryptocurrencies, with regulators imposing stringent guidelines to mitigate potential losses.
Abstract This study conducts a systematic literature review to examine the nexus between financial inclusion and sustainable finance, with a particular emphasis on emerging risks in the digital financial ecosystem. Employing the PRISMA methodology and bibliographic network analysis techniques, including citation network analysis and co-occurrence network of keywords, the review synthesises insights from 54 peer-reviewed publications indexed in Scopus. The findings reveal that financial inclusion significantly advances sustainability by enhancing access to financial services for underserved populations and promoting equitable economic participation. Digital innovations such as fintech, blockchain, and artificial intelligence are shown to facilitate this process, yet they concurrently introduce vulnerabilities, including cybercrime, fraud, and regulatory fragmentation. Islamic finance models, characterised by ethical and transparent financial practises, offer promising solutions for inclusive and secure financial systems. Two thematic clusters are identified: one centred on inclusive and ethical finance and the other on digital innovation in green and sustainable finance. The study underscores the need for integrated strategies that balance accessibility, technological advancement, and risk mitigation. Whilst the exclusive use of the Scopus database represents a limitation, this review contributes to a deeper understanding of how secure, inclusive, and sustainable digital financial systems can support global development goals.
T. C. Jermin Jeaunita, Thiyagarajan Ramesh, C. V. Manjushree, P. T. Shantala
Crowdfunding has become essential for financing entrepreneurial projects, innovative projects, and social initiatives.However, existing platforms face critical challenges, including a lack of transparency, low user engagement, data privacy concerns, and ineffective personalization of user experiences.To address these limitations, this study introduces a novel decentralized crowdfunding framework that integrates Federated Learning (FL), blockchain technology, and Q-learning to enhance security, transparency, and user engagement.The framework leverages FL to collaboratively train models across distributed datasets while ensuring privacy preservation by eliminating the need to share raw user data.Blockchain technology is utilized to ensure tamper-proof transaction records and automate trustless interactions through smart contracts, effectively preventing fraud while increasing transparency.Additionally, a Q-learning-based incentive mechanism is incorporated to predict and stimulate user engagement, ensuring dynamic long-term engagement.The experimental evaluation illustrates that the designed framework attains state-of-the-art performance with an accuracy rate of 99.39%, surpassing existing methodologies.The results demonstrate the effectiveness of the framework in providing a secure, decentralized, and highly personalized crowdfunding system, raising trust and engagement among stakeholders and resolving long-standing issues in crowdfunding platforms.
The complexity of financial decision-making has intensified in the digital era due to data saturation, market volatility, and the inability of conventional models to respond to real-time and non-linear dynamics. Addressing these challenges requires the integration of intelligent systems capable of adapting to evolving financial environments. Smart finance, which combines artificial intelligence, machine learning, big data analytics, blockchain, and automation, offers transformative potential across financial services. This study synthesizes scholarly findings from 2019 to 2024 across five domains: AI-based modeling, robo-advisory applications, behavioral finance integration, decentralized finance (DeFi), and real-time risk analytics. Results indicate substantial gains in efficiency, accuracy, and personalization, yet also reveal persistent challenges, including algorithmic opacity, ethical concerns in data use, and regulatory ambiguity. Advancing smart finance demands development in explainable AI, hybrid advisory systems, and inclusive, adaptive regulation for decentralized infrastructures. The scope of the analysis is limited to peer-reviewed academic literature published in English, excluding industry reports and grey literature.
This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent transition, memory fragment, and reasoning step is committed within a Merkle structure rooted on-chain, rendering it non-repudiable and auditably permanent. To ensure selective access and confidentiality, we derive symmetric encryption keys from ECDH exchanges contextualised by hierarchical privilege lattices. This enforces cryptographic access control over append-only DAG-structured knowledge graphs. Reasoning is constrained by formal logic systems and verified through deterministic traversal of policy-encoded structures. Updates are non-destructive and historied, preserving epistemic lineage without catastrophic forgetting. Zero-knowledge proofs facilitate verifiable, privacy-preserving inclusion attestations. Collectively, this architecture reframes memory not as a cache but as a ledger - one whose contents are enforced by protocol, bound by cryptography, and constrained by formal logic. The result is not an intelligent agent that mimics thought, but an epistemic entity whose outputs are provably derived, temporally anchored, and impervious to post hoc revision. This design lays foundational groundwork for legal, economic, and high-assurance computational systems that require provable memory, unforgeable provenance, and structural truth.
Mihai Duguleană, Andreea Raluca Duguleană, Cristina Tănăsescu, Florin Gîrbacia
In the contemporary robotics landscape, decentralized governance mechanisms offer promising avenues for democratizing control over robotic activities. This paper introduces a new framework integrating crowd-choice mechanisms inspired by Decentralized Autonomous Organizations (DAOs) with robotic control systems. Our framework enables any stakeholder (individuals, companies, public institutions, NGOs and so on) to collectively determine robot activities through transparent and auditable decision-making processes. By leveraging blockchain technology, users can participate in prioritizing and executing robot actions, similar to DAO voting. We outline the architecture, implementation guidelines, and potential applications of the framework across diverse domains such as industrial automation, healthcare, and disaster response. Furthermore, we discuss the governance model, security considerations, and future research directions in advancing decentralized robotics governance. Through this framework, we envision a paradigm shift towards inclusive, transparent, and efficient multi-user robot control.
Supply chain operations have tended to become more complex, thus placing significant pressure on one of the most critical processes: supplier selection and order allocation (SSOA). This process involves a focal company selecting suppliers and allocating orders to obtain required materials. Achieving effective SSOA processes is challenged by (1) reliance on centralized governance and (2) ensuring effective contract management. While so called “smart contracts” could address these challenges, design knowledge about such technology — particularly in the SSOA context — is underexplored in the literature. In this paper we design a smart contract for SSOA in supply chains. We conducted a design science research study and developed three core artifacts: (1) a mathematical description of SSOA; (2) a system model of actor interactions; and (3) SSOA-relevant algorithms. Utilizing the Ethereum blockchain, we demonstrated and tested our smart contracts through scenario analysis. We found that our design is feasible and highly likely to address centralization and effectiveness challenges in SSOA. This paper contributes to the literature by demonstrating how smart contract design focusing on SSOA can further enhance blockchain-driven business models. In addition, we offer prescriptive knowledge on developing smart contracts for SSOA in supply chains.
Thomas Haines, Rafieh Mosaheb, Johannes Müller, Reetika
Zero-Knowledge Proofs (ZKPs) are cryptographic building blocks of many privacy-preserving security protocols. An important research focus in this area is the development of post-quantum ZKPs. These are ZKPs whose security is reduced to computational hardness assumptions that are assumed to be intractable even by scalable quantum computers. In this paper, we study the post-quantum ZKPs of Jain, Krenn, Pietrzak, and Tentes (Asiacrypt 2012). These are the only ZKPs for proving arbitrary binary statements whose security reduces to the Learning Parity with Noise (LPN) problem-a very conservative post-quantum hardness assumption. We make the following contributions to further develop the potential and understanding of these ZKPs. First, we optimize the efficiency of the verifier by several orders of magnitude, making this part as computationally light as that of the prover. Second, we show that the only open source implementation of these ZKPs does not implement them correctly, allowing a malicious prover to convince the verifier of false statements. Third, we formally verify for the first time the security of these (optimized) ZKPs in EasyCrypt. Fourth, we show how these ZKPs can be used to construct the first code-based ZKP of shuffle and verifiable e- voting protocol.
The proliferation of Internet of Things (IoT) devices across critical infrastructure in the United States has created unprecedented security challenges, particularly concerning firmware integrity throughout complex supply chains.This paper presents a novel framework for supply chain integrity verification utilizing distributed ledger technology to establish cryptographic attestation mechanisms for IoT firmware updates.Our research addresses the growing concern over supply chain attacks, exemplified by incidents such as the SolarWinds breach, which demonstrated the catastrophic potential of compromised software distribution channels.The proposed framework leverages blockchain-based attestation to create immutable records of firmware provenance, enabling real-time verification of update integrity from manufacturer to end-device deployment.Through comprehensive analysis of 847 IoT devices across six major manufacturers in the U.S. market, we demonstrate significant improvements in security posture while maintaining acceptable performance overhead.Results indicate a 94.7% reduction in successful supply chain compromise attempts and a 12.3% improvement in update verification time compared to traditional certificate-based approaches.