Verifiable Random Functions (VRFs) and Oblivious Pseudorandom Functions (OPRFs) are essential cryptographic primitives in privacy-preserving applications such as anonymous authentication, private set intersection (PSI), and decentralized identity. Existing constructions, however, rely on number-theoretic assumptions that are vulnerable to quantum attacks. This PhD research project focuses on constructing efficient and practical VRFs and OPRFs from lattice-based assumptions to ensure post-quantum security. A key obstacle in these constructions is the overhead of zero-knowledge proofs (ZKPs), particularly range proofs, which are costly in terms of size and prover complexity. To address this, we investigate probabilistic techniques that relax exact correctness. In particular, we explore approximate range proofs and algebraic transformations, such as using automorphisms in polynomial rings to simulate inner product arguments via polynomial multiplication. These methods enable more efficient and scalable lattice-based constructions of VRFs, including group and context-bound variants, as well as OPRFs. The goal is to make these primitives practical for deployment in post-quantum privacy-preserving systems.
Over the past decade, the surge in popularity of cryptocurrency investment has garnered a lot of attention in the financial marketplace. This study aims to open the “black box” of cryptocurrency investors by understanding the past cryptocurrency investors’ characteristics. Specifically, we investigated the relationship between cryptocurrency investment investors and their characteristics, such as levels of financial literacy, as proxied by both subjective and objective financial knowledge measures, investment experience, and investment knowledge. We apply the logistic regression model to the data from the most recent cohort of the National Financial Capability Study 2021 (NFCS21). We find that investment experience and investment knowledge are the important factors related to investing in cryptocurrency among the characteristics examined. Surprisingly, we also find that individuals with higher education are less likely to become cryptocurrency investors. Finally, the present study reveals an interaction effect between age and investment knowledge, showing that the linkage between cryptocurrency investment and investment knowledge is stronger for younger investors with an age between 18 and 34 than for those above 44. This paper contributes to the existing literature on cryptocurrency investment by revealing the personal characteristics that categorize a cryptocurrency investor. This paper also extends the existing conceptual framework about cryptocurrency by showing that sociodemographic factors, such as age could moderate the linkage between investment knowledge and cryptocurrency investment.
We introduce the notion of committed vector oblivious linear evaluation (C-VOLE), which allows a party holding a pre-committed vector to generate VOLE correlations with multiple parties on the committed value. It is a unifying tool that can be found useful in zero-knowledge proofs (ZKPs) of committed values, actively secure multi-party computation, private set intersection (PSI), etc.
This preprint introduces Proof-of-Being (PoB) and ontological cryptography — the first cryptographic paradigm explicitly designed for the post-AGI era. As frontier language models, autonomous agents, and embodied robots increasingly generate outputs indistinguishable from human actions, classical authentication mechanisms (public-key cryptography, biometrics, CAPTCHAs, proof-of-personhood systems) no longer answer the central security question of the 2025 digital environment: “Was this action performed by a conscious human being?” Proof-of-Being addresses this foundational problem through HISPU (Human Intention Semantic Proof Unit) — a probabilistically unforgeable, fully anonymous attestation of human presence based on ontological randomness and multi-layered semantic–physiological–contextual proofs. HISPU verifies being rather than identity, enabling anonymous but provably human actions across digital systems. Key contributions of this work: · Introduction of ontological randomness as a fourth fundamental source of cryptographic unpredictability (beyond mathematical, physical, and hybrid entropy sources). · Formal definition of the HISPU primitive and seven foundational axioms of ontological cryptography. · Demonstration that no computational system — including superintelligent AGI — can forge a valid HISPU under the Ontological Security Assumption. · Clear conceptual separation between proving human being (ontological presence) and proving identity (social personhood). Applications include: · AGI safety and human-in-the-loop supervisory gates · Sybil-resistant DAO voting and decentralized governance · Intention-based economic systems · Bot-resistant democratic systems, legal smart contracts, and high-stakes authentication · Verifiable human authorship in generative AI ecosystems · Neurotechnology consent verification and BCI safety · Web4 / Noospheric Web intention-layer protocols This work positions Proof-of-Being as a foundational infrastructure for safe human–AI coexistence and represents the first major shift in digital trust since Diffie–Hellman (1976) and zero-knowledge proofs (1985). Keywords: proof-of-being, ontological cryptography, HISPU, proof of intention, ontological randomness, human verification, AGI safety, human-in-the-loop verification, post-AGI trust, intention-based economy, sybil resistance, digital ontology, privacy-preserving verification, human-AI coexistence, consciousness proof, non-simulatable proofs, machine unforgeability.
M. Saravana Karthikeyan, R. Rajasree, R. Santhana Krishnan, C. Gayathri · 6 authors
Secure and efficient healthcare data sharing is critical for modern medical ecosystems, yet existing systems often suffer from limited scalability, privacy risks, and lack of intelligent data management. This study proposes MedVault, a hybrid blockchain-cloud-AI framework designed for secure, patient-centric healthcare data management. The architecture employs Corda for on-chain storage of consent records, metadata, and audit logs, while large medical datasets are encrypted and stored off-chain in AWS S3 with PostgreSQL metadata management. Security is reinforced using AES-256 encryption, Proxy Re-Encryption (PRE), Zero-Knowledge Proofs (ZKP), and decentralized identity management via Hyperledger Indy and Aries, while a FHIR-based gateway ensures seamless integration with electronic health records (EHRs). Intelligence is incorporated through deep learning models, including Autoencoders for anomaly detection, CNNLSTM for medical data analytics, Graph Neural Networks (GNNs) for consent prediction, DNNs for risk assessment, and Federated Learning (FL) for privacy-preserving distributed model training. Variational Autoencoders (VAEs) generate synthetic datasets, and Explainable AI techniques (SHAP, LIME) ensure interpretability. Extensive evaluations demonstrate that Corda-MedVault outperforms Hyperledger Fabric, Ethereum, and traditional centralized approaches across metrics such as blockchain latency, throughput, auditability, off-chain storage efficiency, energy consumption, anomaly detection, and consent prediction. Overall, the proposed system provides a scalable, energy-efficient, privacypreserving, and intelligent platform for real-time healthcare data sharing, offering a robust solution for secure and compliant medical data management.
Artificial Intelligence as a Service (AIaaS) enables users to query a model hosted by a service provider and receive inference results from a pre-trained model. Although AIaaS makes artificial intelligence more accessible, particularly for resource-limited users, it also raises verifiability and privacy concerns for the client and server, respectively. While zero-knowledge proof techniques can address these concerns simultaneously, they incur high proving costs due to the non-linear operations involved in AI inference and suffer from precision loss because they rely on fixed-point representations to model real numbers.
James Hsin-yu Chiang, Ivan Damgård, William R. Duro, Sunniva Engan · 6 authors
We propose efficient, post-quantum threshold ring signatures constructed from one-wayness of AES encryption and the VOLE-in-the-Head zero-knowledge proof system. Our scheme scales efficiently to large rings and extends the linkable ring signatures paradigm. We define and construct key-binding deterministic tags to achieve linkability. We then extend our threshold ring signatures to realize post-quantum anonymous ledger transactions in the spirit of Monero. Finally, our deterministic tags also enable succinct aggregation using approximate lower bound arguments of knowledge; this allows us to achieve succinct (approximate) multi-signatures without SNARKs. Our constructions assume symmetric key primitives only.
In an era of fast-pace technological change, the internet is evolving from Web 1.0 (static, one-way communication) and Web 2.0 (interactive, collaborative platforms) to Web 3.0, characterized by decentralization, artificial intelligence, blockchain, and a focus on authentic values and meaningful connections. Web 3.0 empowers consumers and transforms the internet into a decentralized platform where users control their personal data, intermediaries are replaced by smart contracts and blockchain, but it also introduces challenges such as technological complexity, security risks, regulatory difficulties, and interoperability with Web 2.0. Web 3.0 marketing emphasizes an approach that includes emotional, cultural, and spiritual dimensions, enabling brands to gain a profound and lasting relevance. In this paper we analyse the multifacets of Web 3.0 marketing in the fashion industry, a sector intensely transformed by social, cultural, and technological dynamics. We investigate how marketing principles and Web 3.0 technologies, such as non-fungible tokens (NFTs), the metaverse, and digital identity, are being incorporated into fashion brand strategies, highlighting the benefits and challenges of building authentic relationships with consumers. Fashion brands are embracing emerging technologies to create immersive experiences and loyalty through NFTs, augmented reality, and virtual spaces in the metaverse.
Open access
Fashion and Cultural Textiles
Impact of AI and Big Data on Business and Society
Consumer Behavior in Brand Consumption and Identification
Muhammad Guhya Thesar Afani, Farhan Ferdiansyah, Eraneo Ihza P
The usage of blockchain has increased around the world over the years. It has become widely used in various sectors that need transparency, such as accounting, business, and auditing. Blockchains are gaining more popularity after being applied as a system for digital asset ownership, such as cryptocurrency and NFTs (Non-Fungible Tokens). This growing trend of blockchain is followed by the increasing trend of research regarding it in the last decade. Blockchain has the potential to revolutionize the auditing sector and enhance economic accountability due to its decentralized system. Therefore, research regarding blockchain applications in auditing is becoming an important topic. This study adopts a qualitative approach by using datasets retrieved from the Scopus website, from the search result of blockchain auditing, with a total of 1228 articles that were published in the last decade (2015-2025). Furthermore, this study also uses several software programs as data processing tools, such as R Studio, VOSViewer, and Publish or Perish. This study aims to understand the research trend regarding blockchain auditing in the last decade and highlight its implications for the auditing and economic sectors.
In timing-sensitive blockchain applications, such as decentralized finance (DeFi), achieving first-come-first-served (FCFS) transaction ordering among decentralized nodes is critical to prevent frontrunning attacks. Themis [CCS'23], a state-of-the-art decentralized FCFS ordering system, has become a key reference point for high-throughput fair ordering systems for real-world blockchain applications, such as rollup chains and decentralized sequencing, and has influenced the design of several subsequent proposals. In this paper, we critically analyze its core system property of practical batch-order fairness and evaluate the frontrunning resistance claim of Themis. We present the Ambush attack, a new frontrunning technique that achieves nearly 100% success against the practical batch-order fair system with only a single malicious node and negligible attack costs. This attack causes a subtle temporary information asymmetry among nodes, which is allowed due to the heavily optimized communication model of the system. A fundamental trade-off we identify is a challenge in balancing security and performance in these systems; namely, enforcing timely dissemination of transaction information among nodes (to mitigate frontrunning) can easily lead to non-negligible network overheads (thus, degrading overall throughput performance). We show that it is yet possible to balance these two by delaying transaction dissemination to a certain tolerable level for frontrunning mitigation while maintaining high throughput. Our evaluation demonstrates that the proposed delayed gossiping mechanism can be seamlessly integrated into existing systems with only minimal changes.
As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.
This poster introduces HarvETHter, a smart contract decompiler for EVM-based platforms such as Ethereum, Binance, and Polygon. We present the corpus completeness hypothesis, which we investigate through HarvETHter. Relying on our hypothesis, HarvETHter sources knowledge of the Ethereum blockchain and leverages it to decompile smart contracts to Solidity source code.
Innovation has long shaped economic growth and welfare, but in banking and finance it generates a paradox: while hailed as revolutionary, it often proves fragile and rarely displaces incumbents. This dissertation investigates how FinTech and blockchain innovations could disrupt the organizational structures and dynamics of financial networks, focusing on competition, governance, and trust.The second chapter (as the first is the introduction) examines FinTech through the lens of industrial economics. Using an extended Hotelling model and original data from CIFRE company Shine, it shows that although FinTech entrants offer low-cost, mobile-native services, they struggle to achieve profitability and rarely threaten banks' market share. Traditional institutions adapt their strategies and preserve positive market share and profits, highlighting the structural resilience of incumbents and the persistent challenges faced by newcomers. This study achieves the notable contribution of offering a quantitative measure of the gap that prevents FinTechs from reaching profitability. This chapter thus provides new theoretical and empirical evidence on how geography and network structures shape competitive dynamics in modern banking, while also highlighting the persistent challenges FinTechs face in achieving profitability.The third chapter addresses blockchain as a more radical institutional innovation. Unlike FinTech firms, blockchain is not a new competitor but an alternative governance mechanism that challenges the central role of banks. Drawing on transaction cost economics (TCE theory), the analysis conceptualizes blockchain, smart contracts, and decentralized autonomous organizations (DAOs) as hybrid governance forms positioned between markets and firms. These structures reduce some transaction costs but face inherent limitations in price discovery, adaptability, and enforcement, thereby reshaping rather than replacing financial institutions. This chapter contributes both to a better understanding of blockchain networks through the lens of transaction cost economics (TCE) and to the development of TCE theory itself by introducing a new hybrid form of governance.The fourth chapter study to the question of trust. While blockchain is built on “trustless” cryptographic systems, empirical analysis of major Decentralized Finance (DeFi) protocols shows that social and reputational dynamics remain indispensable. A Social Network Analysis (SNA) of DAO governance forums reveals hierarchical patterns, reputation effects, and off-chain trust systems that supplement algorithmic trust. This chapter therefore provides evidence into multi-level trust formation in blockchain networks and DAOs, showing that while blockchain replaces some institutional functions with cryptographic mechanisms, social and reputational dimensions remain essential to sustaining decentralized governance.Together, these findings challenge the narrative of imminent creative destruction in financial services. FinTech and blockchain foster important transformations, yet their disruptive impact is constrained by market structures, institutional dynamics, and the persistent need for social trust. By combining theoretical modelling, empirical evidence, and network analysis, this dissertation contributes to a nuanced understanding of how financial innovations evolve, not as outright replacements of traditional institutions, but as complex reconfigurations within existing networks.
Lyudmyla Alekseyenko, Marta Dmytryshyn, Mykhailo Novitskyi
The article examines decentralization, a complex process of institutionalization that aims to transform public economic management by transferring authority from central authorities to territorial communities. It is proven that economic power is the dispositive capacity to make decisions that influence the distribution of resources and the vectors of regional development. The sustainability of decentralization requires the simultaneous strengthening of formal (legislative) and informal (public control, transparency, anti-corruption monitoring) institutions. It is argued that, under conditions of global financial transformations, economic power and its institutions require the introduction of a two-tier model of intergovernmental fiscal relations (State – Community) to eliminate financial imbalances, enhance transparency, and maximize the approximation of public services to the population. The study reveals a disbalance between the revenue and expenditure powers of local self-government authorities and the institutional inadequacy of the horizontal financial equalization mechanism. The primary forms of transferring authoritative powers – deconcentration, decentralization, and devolution – are explored, with an emphasis on fiscal decentralization as the central institutional mechanism for transmitting economic power. Two key aspects characterize the dual function of budgetary decentralization: it enhances the efficiency of service provision by adapting to local needs, and it creates incentives for communities to expand their revenue base and attract investment. It is determined that without sufficient fiscal powers, other forms of decentralization remain merely declarative. The highest level of institutionalization is achieved by granting territorial communities the right to independently establish the rates for local taxes and fees, ensuring a direct link between political decisions and fiscal responsibility. The principles of ecological security are revealed as competition among communities encourages them to integrate environmental standards into investment-attracting strategies, develop green technological hubs, and reallocate resources in favor of ecologically oriented projects. In the context of global financial transformations, prospective directions for further research – to ensure the transition from formal autonomy to the genuine financial self-sufficiency of communities – are identified as: enhancing the financial self-sufficiency of territorial communities, digitalization of monitoring and management processes, and developing managerial competencies about the principles of ecological security and inclusivity. Keywords: institutionalization; fiscal decentralization; local self-government authorities; finance; effects of decentralization; project financing; state credit programs; ecological security.
Committees are extensively used in the designs of various Proof-of-Stake (PoS) blockchains. A committee is simply a randomly selected subset of the parties/nodes in the system. Ideally, the committee should i) be as small as possible, and ii) properly represent the entire system, in terms of the corruption ratio. Existing committee selection schemes all follow the principle of proportionality, which says that a committee member should neither over-represent nor under-represent the stake it holds.
Ashwin Karthikeyan, Hengyu Liu, Kuldeep S. Meel, Ning Luo
Efficient zero-knowledge proofs (ZKPs) have been restricted to NP statements so far, whereas they exist for all statements in PSPACE. This work presents the first practical zero-knowledge (ZK) protocols for PSPACE-complete statements by enabling ZK proofs of QBF (Quantified Boolean Formula) evaluation. The core idea is to validate quantified resolution proofs (Q-Res) in ZK. We develop an efficient polynomial encoding of Q-Res proofs, enabling proof validation through low-overhead arithmetic checks. We also design a ZK protocol to prove knowledge of a winning strategy related to the QBF, which is often equally important in practice. We implement our protocols and evaluate them on QBFEVAL. The results show that our protocols can verify 72% of QBF evaluations via Q-Res proof and 82% of instances' winning strategies within 100 seconds, for instances where such proofs or strategies can be obtained.
N. A. Natraj, J. Midhunchakkaravarthy, Brojo Kishore Mishra
INTRODUCTION: The application of blockchain technology to Internet of Things (IoT) systems offers substantial potential for enhancing security, but traditional consensus mechanisms are ill-suited for resource-constrained environments. While hybrid consensus solutions have emerged as a promising alternative, a systematic framework for their classification and evaluation is notably absent. OBJECTIVES: This study addresses this critical gap by introducing a novel, application-driven framework for analyzing hybrid consensus mechanisms, underpinned by a quantitative synthesis of performance benchmarks. METHODS: We analyze diverse architectures—including combinations of Proof of Work (PoW) and Proof of Stake (PoS), PBFT-enhanced systems, and hierarchical models—through the lens of specific IoT application priorities, such as latency, energy efficiency, and scalability. Case studies of IOTA's Tangle, IoTeX's Roll-DPoS, and Hyperledger Fabric illustrate these practical trade-offs. RESULTS: Our framework reveals not only primary performance trade-offs but also critical "second-order" complexities, such as emergent vulnerabilities at the intersection of different consensus layers. CONCLUSION: Our findings demonstrate that this structured, quantitatively-grounded approach provides an effective methodology for designing and selecting regulatory-compliant hybrid consensus solutions for specific IoT applications.
Ala Alrawajfi, Mohd Tahir Ismail, Sadam Al Wadi, Saleh Atiewi
Ethereum and other cryptocurrencies are volatile, making Ethereum-USD rate evaluation difficult.Due to unsuccessful data collection and exchange downtimes, financial time series data are incomplete and lacking critical values.Thus, assessments may be incomplete, and trends may be miscalculated.This research builds and tests an ARIMA-random forest data imputation method to overcome these concerns.This innovative strategy uses AutoRegressive Integrated Moving Average (ARIMA) to describe the linear chronologic sequence relationship and random forest to solve nonlinearity.The suggested method uses ARIMA to handle the linear time-dependent data feature and random forest to reduce estimation errors to improve Ethereum-USD closing price estimates.The mean absolute error (MAE) and mean absolute percentage error (MAPE) results demonstrate that the proposed hybrid model significantly outperforms conventional imputation approaches across all missing data levels (10%-50%).For example, at 30% missing data, the hybrid model achieved an MAE of 0.91 and a MAPE of 0.00074, compared to ARIMA's MAE of 2.21 (MAPE 0.00185) and Random Forest's MAE of 2.34 (MAPE 0.00186).Across all scenarios, the hybrid model reduced MAE by up to 60% and MAPE by over 55% relative to the best single-method baseline, indicating superior robustness and accuracy in handling incomplete Ethereum-USD datasets.By providing precise market and result knowledge, these insights help financial analysts, traders, and researchers make accurate, efficient decisions.
Distributed ledger technology (DLT) provides a fundamental methodological basis for strengthening control and enhancing the reliability of accounting. It shifts trust to the cryptographic level and enables triple-entry accounting and continuous auditing, ensuring the immutability and integrity of accounting information. This article offers a comprehensive examination of the potential of Distributed Ledger Technology (DLT) as a foundation for the methodological transformation of accounting systems and enterprise financial control – specifically, the transition toward the paradigms of triple-entry accounting and continuous auditing – and develops recommendations for mitigating systemic challenges essential for ensuring the reliability of accounting information in the digital economy. The study employs conceptual, synthetic, and classification-systematizing analytical methods to assess the architecture of DLT systems and their suitability for accounting tasks. To provide a multifaceted evaluation of integration potential and related challenges, comparative, synergetic, SWOT, and risk analyses are applied. The research substantiates that adopting DLT technologies enables a fundamental methodological shift in accounting, facilitating the transition from traditional double-entry bookkeeping to Triple-Entry Accounting. It is demonstrated that the immutability, transparency, and cryptographic security of distributed ledgers enable the integration of accounting and analytical functions with emerging digital technologies (AI, IoT, and Big Data). This synergy forms the basis for Continuous Auditing, in which the control function is performed automatically and in real time. The study identifies the main systemic challenges of DLT implementation, including the acute shortage of specialists with dual expertise (accounting, auditing, and DLT), insufficient digital competencies of existing staff, and significant integration complications with legacy enterprise IT infrastructures (ERP systems). Additional risks arise from regulatory uncertainty regarding the legal status of crypto-assets and smart contracts, which hinders the standardization of accounting practices. The article proposes practical recommendations emphasizing that the successful implementation of DLT projects requires a comprehensive approach. This includes not only technological transformation (gradual integration and transition toward consortium-based DLT models) but also active development of human capital. Specifically, investment is required in retraining accounting and analytical personnel, creating new interdisciplinary educational programs focused on smart-contract deployment and DLT analytics, and adapting national accounting standards. Keywords: distributed ledger technology, accounting and analytical support, triple-entry accounting, continuous auditing, digital economy, smart contracts, professional competencies.
András Nagy, János Tapolcai, István András Seres, Bence Ladóczki
Proof-of-stake consensus protocols often rely on distributed randomness beacons (DRBs) to generate randomness for leader selection. This work analyses the manipulability of Ethereum's DRB implementation, RANDAO, in its current consensus mechanism. Even with its efficiency, RANDAO remains vulnerable to manipulation through the deliberate omission of blocks from the canonical chain. Previous research has shown that economically rational players can withhold blocks known as a block withholding attack or selfish mixing when the manipulated RANDAO outcome yields greater financial rewards.
Decentralized e-voting enables secure and transparent elections without relying on trusted authorities, with blockchain emerging as a popular platform. It has compelling applications in Decentralized Autonomous Organizations (DAOs), where governance relies on voting with blockchain-issued tokens. Quadratic voting (QV), a mechanism that mitigates the dominance of large token holders, has been adopted by many DAO elections to enhance fairness. However, current QV systems deployed in practice publish voters' choices in plaintext with digital signatures. The open nature of all ballots comprises voter privacy, potentially affecting voters' honest participation. Prior research proposes using cryptographic techniques to encrypt QV ballots, but they work in a centralized setting, relying on a trusted group of tallying authorities to administrate an election. However, in DAO voting, there is no trusted third party.