The Shipping and Transport Documentation (Bill of Lading - B/L) is a contract between a carrier and a shipper, functioning as a title of ownership in international maritime transport. It is essential in the global trade context but has historically resisted digitalization, resulting in reliance on manual, physical processes and consequently leading to inefficiencies, high operational costs, and vulnerabilities to fraud. This dissertation addresses the dematerialization of the B/L through an architecture focused on a process of data standardization and blockchain technology. The main objective is the development of a system capable of generating electronic B/Ls in an agnostic format, ensuring interoperability between different platforms and unalterable traceability. To this end, the solution adheres to the standards proposed by the Digital Container Shipping Association (DCSA), ensuring that the data model is consistent with the maritime transport industry’s standards. Subsequently, the integrity and life cycle of each electronic B/L are ensured through the registration of its information on a blockchain, which functions as an immutable and distributed ledger. The developed prototype demonstrates that this approach not only facilitates the digitalization of the B/L process but also creates a secure, transparent, and auditable ecosystem, which is essential for the future of international trade.
This chapter explores the intersection between the deterministic execution of smart contracts and the unpredictable nature of delay, a legal phenomenon historically embedded in human discretion and normative flexibility. While smart contracts promise automated, trustless enforcement, they reveal critical vulnerabilities when confronted with unforeseen disruptions, particularly in the context of technical rigidity and legislative gaps. The discussion navigates through the architectural challenges of code literalism, the oracle dependency problem, and the doctrinal limitations of classical contract law in adjudicating delays devoid of intent or culpability. It also examines emerging hybrid legal-technical frameworks, including regulatory innovations in the EU and UK, and the conceptual development of Lex Cryptographica. Ultimately, the chapter proposes a recalibration of contract theory and practice, advocating for a pluralistic approach that integrates technical resilience with normative safeguards to manage delay in a digitally autonomous age.
Η παρούσα μεταπτυχιακή διατριβή εξετάζει τη δυναμική της μεταβλητότητας στις αγορές κρυπτονομισμάτων και αναπτύσσει προβλεπτικά μοντέλα για την εις βάθος κατανόηση της συμπεριφοράς των τιμών σε ένα έντονα κερδοσκοπικό περιβάλλον. Με βάση δεδομένα από τέσσερα βασικά κρυπτονομίσματα, συγκεκριμένα, το Bitcoin (BTC), το Ethereum (ETH), το Litecoin (LITE) και το Dogecoin (DOGE) εφαρμόζονται προηγμένες οικονομετρικές μέθοδοι για την αποτύπωση κρίσιμων χαρακτηριστικών, όπως η συσσώρευση μεταβλητότητας, οι βαριές ουρές κατανομής και οι ασύμμετρες αντιδράσεις σε εξωγενείς διαταραχές. Το μεθοδολογικό πλαίσιο περιλαμβάνει μοντέλα GARCH(1,1) και EGARCH(1,1) με κατανομή GED, ARIMA - GARCH για τον από κοινού προσδιορισμό μέσης τιμής και διακύμανσης, GARCH-X με ενσωμάτωση μακροοικονομικών μεταβλητών, Στοχαστικά Μοντέλα Μεταβλητότητας Bayes (SV), καθώς και ARFIMA για τη διερεύνηση μακροχρόνιας μνήμης.Τα εμπειρικά αποτελέσματα δείχνουν ότι η μεταβλητότητα παραμένει ιδιαίτερα υψηλή σε όλα τα υπό εξέταση κρυπτονομίσματα, με εμφανή φαινόμενα μόχλευσης στο BTC, DOGE και LITE, ενώ το ETH εμφανίζει σχεδόν συμμετρικές αντιδράσεις. Η ενσωμάτωση μακροοικονομικών μεταβλητών αναδεικνύει τον καθοριστικό ρόλο του Δείκτη Τιμών Καταναλωτή (CPI), της προσφοράς χρήματος (Μ2) και των τιμών ενέργειας και τεχνολογίας, κυρίως στην περίπτωση του BTC, επιβεβαιώνοντας τη διασύνδεση των αγορών κρυπτονομισμάτων με τις διεθνείς χρηματοοικονομικές συνθήκες. Οι προβλέψεις ενός βήματος δείχνουν ότι BTC και LITE επιτυγχάνουν την υψηλότερη ακρίβεια, το ETH εμφανίζει μέτριο επίπεδο ακρίβειας, ενώ το DOGE παραμένει ιδιαίτερα απαιτητικό στη μοντελοποίηση, με τον έλεγχο VaR να καταδεικνύει συστηματική υπερεκτίμηση του καθοδικού κινδύνου. Τα Στοχαστικά Μοντέλα Μεταβλητότητας Bayes αποδεικνύονται πιο αποτελεσματικά από τα GARCH στην αποτύπωση αιφνίδιων μεταβολών και αλλαγών καθεστώτος, ενώ τα αποτελέσματα ARFIMA επιβεβαιώνουν περιορισμένη μακροχρόνια μνήμη για BTC και LITE, ήπια επιμονή για ETH και σχεδόν μηδενική για DOGE.Τα ευρήματα υπογραμμίζουν τη σημασία ευέλικτων, ασύμμετρων και βαριάς ουράς μοντέλων για ακριβή πρόβλεψη της μεταβλητότητας στις αγορές κρυπτονομισμάτων. Η μελέτη συμβάλλει στη διεθνή βιβλιογραφία μέσω ολοκληρωμένης συγκριτικής ανάλυσης μοντέλων μεταβλητότητας και προσφέρει πρακτικές κατευθύνσεις σε επενδυτές, διαχειριστές κινδύνου και φορείς χάραξης πολιτικής. Ειδικότερα, προτείνεται η αξιοποίηση προηγμένων μοντέλων για εκτίμηση κινδύνου, η ενσωμάτωση μακροοικονομικών δεικτών στο πλαίσιο πρόβλεψης, καθώς και η υιοθέτηση μετρικών ευαίσθητων στις ουρές κατανομής, όπως το Expected Shortfall, για τη διαχείριση ιδιαίτερα κερδοσκοπικών περιουσιακών στοιχείων.
Anne S. Tsui, Farzam Boroomand, Arjen van Witteloostuijn, Wilfred Mijnhardt
This paper maps how management scholarship has taken up the United Nations' Sustainable Development Goals (SDGs) across the past two decades with a particular focus on how Management and Organization Review (MOR) compares to 18 flagship journals in accounting finance management marketing and operations. Building on a 55⁃year 18⁃journal dataset the authors zero in on 2005—2024—the decade before and after the SDGs' 2015 launch—and add MOR as a 19th journal to assess whether Chinese management research has been especially receptive to SDG⁃oriented work. Methodologically the team uses an ensemble of three AI systems—a keyword / semantic model from Rotterdam School of Management OpenAI GPT⁃4. 1 and Claude Sonnet 3. 7—to score each article abstract against all 17 SDGs. Articles are tagged to a goal when at least two models concur (“majority rule”) allowing multi⁃label assignment. Inter⁃model agreement is high (most pairwise correlations > 0. 90) and the resulting SDG ratio—the share of a journal's output mapped to at least one SDG—serves as a transparent scalable indicator of a journal's social⁃value orientation. Across the 20⁃year window SDG engagement rises markedly after 2015. In the 18 journals the SDG ratio climbs from a pre⁃2015 baseline of 9% to 31% in 2015—2024. MOR exhibits both higher levels and stronger growth 28. 4% of its 2005—2014 papers are SDG⁃linked jumping to 43. 3% post⁃2015—about 14 percentage points above the contemporaneous 18⁃journal average. Aggregated over 2005—2024 36. 7% of MOR's 365 articles map to at least one SDG compared with 26. 4% of the 24,508 articles in the comparison set indicating a consistently stronger SDG orientation at MOR. Topic coverage is uneven but broadly aligned across journals. Four goals dominate in both MOR and the 18 journals SDG08 (Decent Work and Economic Growth) SDG09 (Industry Innovation & Infrastructure) SDG10 (Reduced Inequality) and SDG16 (Peace Justice & Strong Institutions). MOR also shows attention on SDG12 (Responsible Consumption & Production) clearing the 1% threshold there whereas the 18⁃journal group surpasses MOR on SDG03 (Good Health & Well⁃Being) and SDG05 (Gender Equality). Several ecology⁃focused goals (e. g. SDG 13—15) remain comparatively underrepresented overall underscoring opportunities to bind environmental stewardship more tightly to mainstream management theories of strategy organizing and innovation. The findings illuminate the agenda⁃setting power of editorial policy. MOR's mission—to advance theory from and about China while cultivating humanistic stakeholder⁃oriented inquiry—appears to institutionalize stronger incentives for socially consequential work through topic selection special issues and review criteria. This suggests that journals can accelerate the field's pivot toward responsible research without sacrificing rigor echoing the Responsible Research in Business and Management (RRBM) movement's dual mandate of credibility and usefulness. The paper also positions the SDG ratio as a complementary metric to citations—one that foregrounds societal relevance. While an SDG tag is not proof of real⁃world impact systematic SDG mapping offers a common language for scholars editors and funders to monitor progress identify blind spots (notably climate and biodiversity) and align resources and evaluations with global development priorities. Methodologically the study endorses AI ensemble triangulation as a reliable scalable approach for large⁃corpus content analysis with the caveat that multi⁃model checks and transparency are essential. In sum management research has shifted—unevenly but decisively—toward societal stewardship since 2015. MOR stands out as a field leader demonstrating how editorial stewardship can galvanize SDG⁃relevant scholarship. The road ahead is clear deepen coverage of neglected ecological and equity goals maintain methodological pluralism and use SDG⁃aligned incentives to translate rigorous scholarship into knowledge that advances the common good.
Abdullah Mubarak Al Dhaheri, Mohammad Amin Alkrisheh, Tayil Mahmoud Shiyab, Ibrahim Al Nuaimi
This research addresses the issue of the illicit use of digital cryptocurrencies, considering it one of the most pressing contemporary legal challenges facing legislative systems, particularly given the unique technical features of these currencies-such as encryption, decentralisation, and anonymity. The significance of the study lies in examining the legal impact of cryptocurrencies on cybersecurity and analysing the adequacy of the legal framework in the United Arab Emirates in confronting crimes arising from their use. The study's core problem lies in the absence of a comprehensive legislative framework that regulates the use of cryptocurrencies and limits their exploitation in cross-border crimes, such as money laundering and terrorist financing. It also lies in the technical difficulties of tracking digital transactions and the lack of well-established legal concepts regarding the possession of cryptocurrencies and the liability of those dealing with them. The research adopts an analytical methodology through the study of relevant legal texts, in particular, Federal Decree-Law No. 20 of 2018 on Anti-Money Laundering and Combating the Financing of Terrorism and Illegal Organisations; Federal Law No. 34 of 2021 on Combating Rumours and Cybercrimes; and Law No.4 of 2022 on the Regulation of Virtual Assets in the Emirate of Dubai. The study concludes that the current legislative structure is advanced at the regional level but requires further specialisation and technical flexibility. The study recommends issuing a federal law addressing digital assets, enhancing international cooperation in information exchange, adopting technical solutions such as artificial intelligence to track digital crimes, and equipping the competent authorities with advanced legal tools to regulate transactions in this field.
The rapid rise of Bitcoin ignited a global frenzy over cryptocurrencies, driving a surge in their issuance and investment. Unprecedented returns on these assets have led to comparisons with irrational exuberance. Against this backdrop, and adopting a behavioural finance perspective, this thesis thoroughly investigates the role of investor sentiment in cryptocurrency prices and volatility across three empirical studies. Sentiment is proxied by the Fear and Greed Index (FGI), published by Alternative.me, and the Economic News Sentiment Index (NSI), developed by the Federal Reserve Bank of San Francisco. The first study examines volatility connectedness among six Bitcoin (BTC) currency pairs and identifies its determinants. The results indicate that BTC/USD and BTC/GBP are major volatility transmitters, while BTC/USDT remains relatively isolated. Key determinants include trading volume, sentiment, economic policy uncertainty, and gold volatility. There is an asymmetric effect of sentiment derived from the FGI: optimistic sentiment intensifies volatility connectedness, whereas pessimistic sentiment dampens it. Policy uncertainty and gold volatility are positively associated with spillover intensity, with cryptocurrency-related events further shaping the degree of connectedness. The second study focuses on intraday cross-exchange (i.e., Bitfinex and Kraken) price discovery for Bitcoin and Ethereum, along with its potential drivers. The results show that Bitfinex dominates price discovery during most periods, but the pandemic alters this process. We identify a shift in drivers, with market quality factors losing significance and news sentiment emerging as a more prominent influence in the post-pandemic period. During episodes of heightened news sentiment, Bitfinex consolidates its leading position. Additionally, Ethereum’s price discovery is significantly associated with intraday volatility. The third study assesses whether the inclusion of the FGI in GARCH(1,1), EGARCH(1,1), and HAR(1,7,30) models improves the accuracy of volatility forecasts for twelve cryptocurrencies. The findings suggest that incorporating the FGI enhances predictive performance, with variation across assets. Bitcoin, Ethereum, and cryptocurrencies technologically linked to them (e.g., Litecoin, DASH and Ethereum Classic) benefit from the integration of sentiment in most cases. EGARCH+FGI and HAR+FGI consistently outperform competing models, particularly at the weekly forecast horizon. This thesis provides behavioural finance insights for cryptocurrency investors and policymakers. Investors may leverage the FGI and the NSI to refine trading strategies and inform venue selection, while policymakers may incorporate the FGI into monitoring frameworks to better identify systemic risks and anticipate excessive cross-market spillovers.
In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLab’s main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab.
Cryptocurrency is an alternative payment method developed with encryption techniques. To predict Bitcoin values using both weekly and monthly datasets, this study compares four machine learning models: GRU, Weighted LSTM, LSTM, and LSTM with Attention. The models' accuracy and dependability in capturing the dynamics of cryptocurrency prices were assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (RSCORE). While LSTM with Attention did well with an RSCORE of 0.7173, LSTM with Attention had the highest RSCORE of 0.9173 in the weekly dataset, indicating higher ability in modelling short-term sequential patterns. Additionally, weighted LSTM performed well (RSCORE of 0.8002), surpassing GRU (RSCORE of 0.5728), which had trouble keeping up with the volatility of Bitcoin prices. Both LSTM and LSTM with Attention performed best in the monthly dataset, each with the lowest MSE (0.0304) and an RSCORE of 0.8173. With an RSCORE of 0.7002, weighted LSTM came next, using temporal weighting to enhance predictions. Because of its limited capacity to grasp intricate temporal connections, GRU continuously fared poorly in both datasets. According to the analysis, LSTM is the most dependable model for both short-term and long-term forecasts, and for weekly forecasts, LSTM with Attention provides improved interpretability. These results provide a framework for applying machine learning approaches to financial time series forecasting, highlighting the significance of choosing suitable models based on data frequency, volatility, and prediction aims.
Venkateswarlu Boddu, Malaya Dutta Borah, Naresh Babu M
This paper proposes a blockchain-based framework to monitor drugs and trigger alerts to safeguard stakeholders when drugs are reported to be faulty. The proposed system uses a decentralised approach, enhancing the overall transparency and security. The framework identifies counterfeit drugs and reports the issue across various stakeholders in the pharmaceutical supply chain, including regulatory departments, hospitals, and manufacturers. The alerts regarding contaminated or counterfeit drugs are ensured due to the integration of blockchain and distributed ledger. Furthermore, the recorded data are secure, traceable, and immutable. The results demonstrate that the framework successfully registers the departments and sends the alert across the departments associated with the blockchain. Also, the results confirm the feasibility of using blockchain to create a transparent and robust counterfeit drug alert network, providing a reliable solution for pharmaceutical safety management.
Pharmaceutical Quality and Counterfeiting
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Amid growing global urgency for climate action, innovative financial mechanisms are critical for advancing renewable energy transitions in developing economies. This study investigates the role of financial technology (fintech), with a focus on foreign portfolio investment (FPI), in influencing renewable energy investment (REINV) across 54 developing countries in Africa, Asia, and Latin America from 2010 to 2023. Employing a multi-method empirical approach, comprising Spatial Durbin Models (SDM), Quantile Regression (QR), Stochastic Frontier Analysis (SFA), and Spatial Quantile Regression (SQR), the research captures spatial dependencies, distributional heterogeneity, and efficiency dynamics. The SDM results indicate that FPI significantly increases REINV both directly (1.112) and indirectly through spillover effects (0.445), supported by significant spatial autocorrelation (0.334). Economic development and institutional quality also play key roles, with GDP per capita and institutional quality exerting positive and significant direct effects. Quantile regression reveals that FPI has a stronger influence at higher quantiles of REINV, with coefficients rising from 0.745 to 1.445, highlighting distributional inequality in fintech impact. SFA results show that FPI also enhances technical efficiency (0.912), though diminishing marginal returns are evident. Greater financial depth and electricity access reduce inefficiency, while inflation worsens it. Spatial quantile regression further confirms that regional spillovers are more pronounced among high-investment countries, underscoring the role of spatial dynamics in clean energy financing. The findings suggest that fintech can be a catalyst for renewable energy growth, especially in countries with higher institutional and financial capacity. Policy recommendations include strengthening digital infrastructure, enhancing regulatory coordination, and ensuring macroeconomic stability to fully leverage fintech's potential. Future research should explore emerging fintech tools such as decentralized finance and blockchain-based green bonds.
A segurança de contratos inteligentes continua sendo um desafio na blockchain Ethereum. Este artigo investiga a evolução de ferramentas de análise de segurança por meio de dois experimentos com a estrutura SmartBugs. O primeiro analisa 215 contratos do Etherscan verificados recentemente, focando nas vulnerabilidades detectadas. O segundo replica um estudo de 2020, usando o mesmo conjunto de contratos com vulnerabilidades, mas com ferramentas atualizadas. Resultados indicam defasagem da taxonomia DASP Top 10 e uma queda na precisão de detecção (de 41,7% para 24,3%), levantando dúvidas sobre o real progresso das ferramentas.
There has been a growing need for secure, transparent, and efficient systems to manage agricultural processes. Traditional methods often lack traceability, leading to inefficiencies, fraud, and data manipulation. Additionally, centralized agricultural management systems are prone to security risks and single points of failure. Blockchain technology provides a decentralized and secure solution that enhances transparency, security, and efficiency in agriculture. By utilizing a distributed ledger, blockchain ensures end-to-end traceability of agricultural products, preventing fraud and ensuring quality control. Farmers, suppliers, and consumers can verify the authenticity of products without relying on intermediaries, thereby reducing costs and enhancing trust in the supply chain using the proposed scheme.
Machine learning as a service (MLaaS) has emerged as a prominent computing paradigm where users send sensitive data to cloud servers that subsequently return computed results. In MLaaS, ensuring the correctness of these results poses a significant challenge. While zero-knowledge proof (ZKP) presents a potential solution, they often come with substantial memory overhead. Moreover, there is insufficient attention given to the privacy risks associated with untrustworthy servers, which could jeopardize users' sensitive information. In this paper, we introduce$\text{Vp}^{3}\text{CNN}$, a three-party verifiable privacy-preserving convolutional neural network (CNN) inference scheme. In$\text{Vp}^{3}\text{CNN}$, users verify the correctness of CNN inference through a lightweight ZKP protocol grounded in vector oblivious linear evaluation. This protocol is designed to ensure that servers incur minimal memory overhead while maintaining the integrity of the verification process. Based on the optimization of the convolutional relation, the scheme reduces the computational cost associated with the verification process of the convolution operations. In addition,$\text{Vp}^{3}\text{CNN}$employs two non-colluded servers to protect user data privacy via secret sharing schemes. We implement our scheme in C++ and evaluate its performance using the MNIST and CIFAR-10 datasets. Experimental results demonstrate that, compared to existing methods,$\text{Vp}^{3}\text{CNN}$achieves a speedup of 4–5 times in convolution verification while maintaining nearly consistent communication overhead. Importantly,$\text{Vp}^{3}\text{CNN}$does not compromise the accuracy of CNN inference, achieving an accuracy of 97.8% on the MNIST dataset.
Prizadevanje za vzpostavitev evropskega okvira za digitalno identiteto je leta 2024 doprineslo do pomembnega koraka naprej, saj je 20. maja 2024 začela veljati novela EU uredbe št. 910/2014 za e-identifikacijo in storitve zaupanja, ki jo poznamo tudi kot Uredba eIDAS 2.0. Ta vzpostavlja pravno podlago za uvedbo evropske denarnice za digitalno identiteto po vsej EU. Z denarnico bodo uporabniki lahko tudi varno pridobili, shranili in delili svoje pomembne dokumente, npr. o izobrazbi in licencah, pooblastila za zastopanje pravnih oseb, finančne podatke in podatke o družbah, ter elektronsko podpisovali oz. v primeru denarnic za podjetja elektronsko žigosali dokumente. Da bi dosegli interoperabilnost med denarnicami, izdanimi s strani držav članic, so v izvedbenih aktih k Uredbi eIDAS 2.0 določeni standardi za evropsko denarnico, ki jih morajo upoštevati vse implementacije denarnic po državah, pravila za certificiranje denarnic in sporočanje Evropski komisiji. Skupne zahteve za denarnico se pripravljajo v okviru Arhitekturnega in referenčnega okvirja (ARF), poleg tega pa Evropska komisija pripravlja tudi referenčno implementacijo denarnice. V prispevku so podrobneje predstavljene nekatere visokonivojske zahteve ARF, ki se nanašajo na področje zasebnosti, še posebej uporaba metod ničelno spoznalnih dokazov (angl. Zero knowledge Proof) za zagotavljanje zasebnosti v ekosistemu denarnic.
The intersection of Blockchain and Artificial Intelligence (AI) holds the potential to revolutionize the way smart contracts are deployed and managed. Blockchain provides decentralization, immutability and transparency however its deterministic and inflexible characteristics make it challenging to adapt to dynamic and rapidly changing environments. On the other hand, AI can provide the ability to do things such as pattern recognition, predictive analytics, and intelligent decision-making, but lacks the trust and verifiability that a blockchain can provide. This article presents research on the incorporation of AI in blockchain-powered smart contracts to improve trust, operational efficacy, and execution precision. We propose a new type of architecture where AI agents run in or adjacent to smart contracts to optimally set the conditions, automatically resolve disputes, detect anomalous behavior, and validate external data using oracles and machine learning models in real-time. By illustrating AIenabled smart contracts in domains such as supply chain management and decentralized finance (DeFi), we show how the use of AI can improve the performance of smart contracts by lowering latency, eliminating fraudulent triggers of contracts and the ability for contracts to adapt based on context-aware inputs without having to sacrifice the integrity and auditability native in decentralized systems. These two aspects of performance, namely, gas cost reduction, error rate minimization, and contract adaptability, are studied across the domains, or environments, of public and permissioned blockchains. The paper further addresses hurdles considering AI interpretability, on-chain computational limits, and the necessity for uniform standards for AI-blockchain interfacing. By combining the trust layer of blockchain with the cognitive layer of AI, we unlock a new realm of smart contracts - those that are not only self-executing but also self-optimizing and contextually aware.
Rad analizira digitalnu transformaciju u industriji osiguranja s posebnim naglaskom na primjenu blockchain tehnologije i pametnih ugovora. Istražuje kako telemetrija i oracle tehnologija omogućuju prikupljanje i korištenje podataka iz stvarnog svijeta za dinamično oblikovanje ugovora o osiguranju, što vodi razvoju novih modela poput mikroosiguranja, peer-to-peer osiguranja i osiguranja temeljenog na stvarnoj uporabi. Rad također razmatra pravne aspekte pametnih ugovora, njihovu pravnu valjanost, ograničenja u interpretaciji, te izazove u zaštiti privatnosti i regulatorne izazove koje donosi njihova primjena unutar EU i Republike Hrvatske. Poseban naglasak stavlja se na važnost stvaranja jasnih i prilagodljivih pravnih rješenja koja će omogućiti odgovornu i učinkovitu integraciju novih tehnologija u osigurateljnu praksu.
In today's rapidly evolving landscape of smart city applications, particularly in sensitive areas like the healthcare sector, safeguarding the security, integrity, and privacy of data has become a significant and challenging concern. Specifically in the healthcare sector, the sharing and access of patient records across various stages of care by doctors, nurses, pharmacies, and diagnostic centers introduce new complexities and potential vulnerabilities. However, these challenges intensify more in the case of distributed healthcare networks where data is fragmented across institutions. This work addresses issues such as data vulnerability and misuse in distributed healthcare environments by proposing a Blockchain-enabled Distributed Healthcare System (BeDHS). The model is designed to facilitate secure, transparent, and privacy-preserving collaboration among healthcare entities. It adopts a hybrid approach, integrating a quantum key-based image encryption technique to enhance the security of health records. The encrypted images are securely stored in the InterPlanetary File System (IPFS) to ensure data integrity and availability. Additionally, a Federated Learning (FL) framework is employed to enable collaborative training of AI models across institutions without exposing sensitive patient data. The proposed BeDHS model is implemented using Solidity-based smart contracts on the Ethereum blockchain, ensuring decentralized and tamper-resistant operations. Simulation results demonstrate that the proposed model outperforms existing healthcare data management systems in terms of efficiency and security. • A blockchain-enabled distributed healthcare system is proposed, where the number of healthcare institutions of a smart city are integrated to form a collaborative and transparent model for sharing health records while maintaining security, privacy, and immutability. • A Quantum-Chaos-Encryption cryptographic technique integrated with blockchain for protecting digital documents and medical images from unauthorized access. • To build a privacy-preserved distributed-collaborative healthcare system, a federated learning approach is incorporated that trains the AI models directly at the data source of multiple healthcare institutions while eliminating the need to transfer between the institutions.
Elections and referendums play a vital role in a democratic society, which enable individuals to make collective decisions. In the Internet information era, electronic voting has replaced traditional paper voting. However, the centralized architecture of the electronic voting system is vulnerable to attacks and the voting records can be easily changed or even deleted. Blockchain, as a decentralized and trustworthy distributed network, offers new means for electronic voting systems. Current blockchain- based voting systems still face several challenges: they cannot achieve full verifiability in self-tallying, cannot tolerate invalid or abstained ballots, and cannot prevent Sybil attacks either. To address these challenges, we use some cryptographic primitives to construct a blockchain- based decentralized self-tallying verifiable referendum scheme to provide a transparent and secure remote electronic voting system. First, we use range zero-knowledge proofs to verify the ballot content, and for the first time, propose a novel method using bilinear pairing to verify decryption results, which significantly reduces computational burden and gas consumption during verification. Second, we ingeniously combine a threshold decryption system with a blockchain-based deposit mechanism: invalid or abstained ballots are excluded from the tally, and voters casting such ballots are incentivized to publish their partial private keys through the deposit mechanism, ensuring their exit from the decryption process without disrupting the election. We also establish an innovative access mechanism for smart contract that effectively prevents Sybil attacks. Theoretical analysis and experimental results demonstrate that our system is secure, feasible, and efficient.
The merging of Artificial Intelligence (AI) with the Internet of Things (IoT) has sparked a swift transformation in AIoT systems, allowing for real-time intelligence in smart cities, industries, and homes. Yet, these advancements bring about increasing worries regarding data privacy, device trust, and potential security threats-particularly with the emergence of quantum computing. This paper introduces a secure and privacy focused AIoT framework that integrates Federated Learning with Differential Privacy, Zero-Knowledge Proofs (ZKP) for device authentication, and Post-Quantum Cryptography(CRYSTALSKyber) to protect model updates on the blockchain. Unlike conventional methods that depend on cloud processing and expose sensitive data, this innovative system allows for on-device model training through TinyML, ensuring that data remains on the device. A practical implementation using ESP32-S3 devices in both a smart classroom and home environment showcases the framework's effectiveness. The results indicate a 12% boost in privacy, a 35% reduction in communication costs, and an 8.7% increase in model accuracy compared to traditional methods. This architecture tackles significant unresolved challenges in AIoT by securing data at the edge, preventing device spoofing, and preparing for future quantum threats-making it an excellent choice for privacy-sensitive, real-time AIoT applications.
Mehmet Ali Aygül, Hakan Ali Çırpan, Hüseyin Arslan
This paper proposes a novel multi-party key generation method that jointly utilizes channel state information (CSI) and blockchain technology to enhance security in distributed systems. The proposed method starts by extracting CSI from wireless channels, leveraging the channels’ inherent randomness and reciprocity to generate secure key fragments shared among legitimate parties. Then, the key generation process involves several stages, including quantization, reconciliation, and privacy amplification, ensuring that the resulting keys are secure and synchronized across participants. Blockchain technology is then leveraged to securely commit these keys, ensuring that the key agreements are recorded in a decentralized, tamper-resistant ledger. The proposed method effectively combines the physical-layer properties of CSI with the decentralized nature of blockchain, providing robust protection against eavesdropping and tampering attacks. Theoretical analyses and simulation results demonstrate the effectiveness of the proposed method in terms of key mismatch probability and secrecy capacity. Additionally, the randomness of the generated keys by the proposed method is validated using the National Institute of Standards and Technology randomness tests.
Srinivas. D, K. K. Rawat, Jiwanjot Kaur Hira, Sangeeta Bagga · 6 authors
The digital transformation of financial systems demands solutions that are secure, scalable, and capable of real-time processing. This paper presents an integrated framework that combines sixth-generation (6G) communication networks, blockchain technology, and artificial intelligence (AI) to address the growing challenges in financial data security and integrity. The proposed architecture leverages the low-latency and high-reliability features of 6G, the immutability of blockchain, and the adaptive capabilities of AI to support secure and automated financial operations. Key components include a layered network model, AI-driven anomaly detection, and privacy-preserving techniques such as federated learning and secure multi-party computation. The study outlines practical use cases including cross-border payments, central bank digital currencies (CBDCs), smart insurance, and decentralized identity verification. It also discusses limitations in scalability, regulatory compliance, and system interoperability. Future directions include quantum-resilient systems and the deployment of autonomous agents for real-time governance. The findings contribute to the design of secure and adaptive infrastructures for next-generation financial services.
Blockchain has evolved from cryptocurrency infrastructure to a foundation for decentralized finance, supply chain, and digital identity. However, widespread adoption faces three main barriers that are high energy use from traditional consensus, fragmented networks, and static, rule-based smart contracts. This work presents EcoChainX, a modular framework integrating AI-driven automation, sustainable consensus, robust interoperability, and advanced privacy features. Its four-layer architecture consists of AI modules for anomaly detection and smart contract optimization, energy-efficient consensus protocols, cross-chain interoperability, and privacy-preserving technologies such as zero-knowledge proofs and quantum-resistant cryptography. Through theoretical modeling, prototyping, and empirical testing, EcoChainX addresses scalability, sustainability, security, and privacy. By addressing them, this framework paves the way for responsible blockchain ecosystems capable of supporting the next generation of decentralized applications. Empirical results demonstrate that EcoChainX achieves a 97% reduction in energy consumption compared to traditional Proof-of-Work systems, increases transaction throughput by over 20 times (exceeding 10,000 TPS), reduces smart contract vulnerabilities by 60 % through AI-driven anomaly detection, and enables cross-chain transactions with a latency reduction of 80 %, establishing a new benchmark for sustainable and interoperable blockchain infrastructures.
Elliptic curve-based zero-knowledge proof (ZKP) protocols typically use multi-scalar multiplication (MSM) as a key primitive, making it one of the major performance bottlenecks in real-world ZK provers. In this paper, we present an FPGA-based MSM accelerator that achieved state-of-the-art performance in the 2023 ZPrize, a competition dedicated to advancing zero-knowledge cryptography, with submissions from both academia and industry. Our design achieves this through two primary innovations. First, we adopt affine (two-coordinate) representations for elliptic curve points, rather than resorting to projective coordinates, and leverage a batched inversion strategy to handle the expensive multiplicative inverse operation. Although many implementations extend points to projective form to avoid explicit inversions, they incur additional multiplications. By retaining affine coordinates and using the Montgomery trick (where multiple denominators are inverted at once), our accelerator reduces the overall number of real inversions per batch of point additions, drastically improving throughput while preserving a simpler coordinate system. Second, we introduce a novel hazard avoidance scheme that eliminates pipeline stalls arising from our high-latency elliptic curve addition pipeline. Through early detection and reordering of hazards, the pipeline remains fully utilized, thus maintaining continuous high throughput.
The use of Enterprise Data Warehouse (EDWs) has been experienced as the analytical backbone of risk management, financial reporting and regulatory reporting of the data in very regulated sectors like banking, insurance, and capital markets. They were based on batch-oriented Extract Transform Load (ETL) paradigms, tight coupled schema and monolithic governance models that are better suited to stability than agility. Nevertheless, the increasing regulatory complexity, impacts of the near-real time risk visibility requirements, and increasing cost of infrastructure have emanated inherent weaknesses of the legacy EDW architectures. At the same time, the emergence of hybrid cloud platforms, scalable object storage, distributed query engines, and workflow orchestration system has made it possible to make the paradigm shift toward Extract–Load–Transform (ELT), domain-driven data products, and decentralized ownership models. In spite of these developments, in numerous organizations, the pressure to modernize reporting pipes based on strong backward compatibility criteria, audit limitations and the operational risks of massive data migrations makes this a challenge. This paper gives a detailed blueprint of modernization in the process of moving the old EDW centric ETL architectures to the hybrid cloud ELT platforms to suit the risk, finance, and regulatory reporting. Its proposed solution integrates domain-driven data products and ELT pushdown transformations orchestrating control planes and explicit data contracts that is applied in an incremental fashion with a strangler pattern. The framework focuses on retrogressively compatible schemas, reconcilability determinacy, the rollback safety nets, and regulated cutover plans to provide continuous regulatory compliance. Using a well-organized migration roadmap, cost and performance metrics and an official risk register, the paper will show how organizations can shorten report delivery cycles, enhance service-level agreement (SLA) compliance and minimize the overall cost of ownership without sacrificing auditability and strict governance. The findings have shown that hybrid cloud ELT systems may cut the latency in report by more than 40%, cut compute expenditure by up to 35, and become much more responsive to regulatory cases without infection of information integrity or resilience.