Giulio Segalini, Yigit Çolakoğlu, Marko Putnik, Jérémie Decouchant
Decentralized Finance (DeFi) applications rely heavily on the order in which transactions are executed, making them susceptible to reordering attacks that enable adversaries to extract Blockchain Extractable Value (BEV). While linear blockchain systems such as Ethereum have inspired extensive research into fair ordering mechanisms, DAG-based consensus protocols have remained largely unprotected despite their growing adoption for scalability and performance. In this paper, we introduce Tilikum, a DAG-based ledger protocol that ensures fair transaction ordering without relying on weak edges. Tilikum achieves ordering linearizability by leveraging median-based timestamp aggregation, or batch order fairness, while maintaining low data redundancy and robust garbage collection. We implemented Tilikum in Rust and evaluated it against representative baselines, namely Narwhal/Tusk, Pompē, Themis and FairDAG. Our results show that Tilikum achieves up to $39\times$ higher throughput than other fair-ordering baselines, while fully blocking state-of-the-art DAG-specific reordering attacks.
India’s Unified Payments Interface (UPI) gates transaction limits behind Know Your Customer (KYC) compliance tiers mandated by the Reserve Bank of India (RBI) and National Payments Corporation of India (NPCI). Unlocking the Full KYC tier currently requires users to surrender sensitive identity documents (Aadhaar, PAN, income proofs) to Payment Service Providers (PSPs). This centralized storage creates severe breach vulnerabilities and systemically violates the data minimization principle of India’s Digital Personal Data Protection (DPDP) Act 2023, Section 8(3). We present ZKProof-eKYC, the first Zero-Knowledge Proof (ZKP) framework designed specifically for payment system tier access control. By reframing KYC eligibility as a cryptographic access token, a user’s device generates a 1.5 KB non-interactive Groth16 zk-SNARK proof asserting tier eligibility. The PSP receives only a boolean result, eliminating personal data transmission and achieving DPDP Act compliance mathematically. The primary contribution is a multi-predicate Circom 2.0 circuit (≈25,000 R1CS constraints) simultaneously enforcing eleven regulatory predicates (ϕage to ϕtier) mapped across six Indian statutes. The architecture introduces five key elements: (i) an 8-leaf depth-3 Poseidon Merkle credential tree; (ii) a dualdocument commitment scheme protecting the raw PAN (singlehash) and Aadhaar (double-hash) identifiers; (iii) an EdDSAPoseidon issuer signature; (iv) a depth-20 Sparse Merkle Tree (SMT) for real-time revocation; and (v) Poseidon nonce-binding against replay attacks. A novel branch-free finite-field formula calculates NPCI’s tier limits natively: tier = 2 · ⊮[FullKYC] + (1−⊮[FullKYC])·⊮[MinKYC]. We deploy a dual-circuit framework: UPIKYCTierProof for Full KYC and MinKYCTierProof for Min KYC. Functional correctness is validated against 12 adversarial test vectors. Performance profiling projects mobile WASM generation at <400 ms, with off-chain execution measured at ≈96 ms and on-chain verification at ≈242,000 gas. ZKProofeKYC establishes the first “one credential, multiple products” ZKP architecture for national payment infrastructure.
The paper investigates the problem of ensuring confidentiality in authentication processes within enterprise information-intelligent systems under increasing cybersecurity threats and growing requirements for data protection. The introduction substantiates the relevance of modern cryptographic approaches that minimize the transmission of sensitive information during user authentication. The literature review analyzes approaches to constructing zero-knowledge proofs, which enable verification of a statement without revealing secret data, including succinct non-interactive arguments of knowledge, transparent scalable arguments of knowledge, and compact proof systems without trusted setup. Their cryptographic properties, trust assumptions, scalability, and computational characteristics are examined. In the methodology section, an adaptive authentication model is proposed, based on the integration of cryptographic proofs with risk assessment mechanisms and contextual access analysis. A formal decision-making model for access control is developed, taking into account user parameters, environmental characteristics, and threat levels, enabling dynamic selection of the proof type depending on the current risk level. An authentication algorithm is designed, including stages of identification, context evaluation, proof generation, and verification. In the results section, a comparative analysis of different types of zero-knowledge proofs in enterprise systems is conducted, evaluating their impact on performance, security level, and resistance to attacks. It is shown that the adaptive approach ensures a balance between cryptographic strength and computational efficiency. The conclusions justify the feasibility of implementing the proposed model as part of modern continuous access verification concepts and as a means of improving enterprise information security.
Miners of proof-of-work networks like Bitcoin tend to gravitate towards regions with cheap energy. We analyze risks associated with this geographical centralization by exploiting a local electricity supply shock. Compared to a control group consisting of an energy-efficient proof-of-stake cryptocurrency, the blockchain’s capacity for processing transactions decreases while transaction fees increase substantially. The increased settlement latency on the blockchain also reduces secondary market quality as seen in higher exchange rate volatility, lower liquidity, and larger price differences between exchanges. Overall, our results suggest that geographical centralization poses short-lived but potentially severe system-wide risks to proof-of-work networks.
Jintao Huang, Fengqing Jiang, Radha Poovendran, Zhiqiang Lin
We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis. Built from 541 real-world exploit incidents from DeFiHackLabs spanning 9 EVM chains, the benchmark provides end-to-end on-chain evaluation where agents interact with historical blockchain state through isolated evaluation environments orchestrated by Harbor, using tools to read code, trace transactions, and validate exploits on mainnet forks. Each case is anchored to a specific block and includes structured ground truth covering vulnerability type, localization, and attacker profit. Exploits are graded by economic impact on historical forks; patches are validated by replaying historical attacks and legitimate transactions as fail-to-pass test oracles on a proxy-upgradeable subset. We define a five-type vulnerability taxonomy and evaluate multiple agent--model configurations. Results reveal a clear difficulty gradient: the best configuration scores 37.5% on detection, 43.7% on exploitation, but only 23.4% on patching, with the top agent (Codex with GPT-5.5) realizing \$57.4M in total exploit profit across the 200-case exploit set at a cost of $2.39 per case.
Decentralised systems are often built to avoid central control, but their evolution almost always depends on centralised platforms, informal maintainer authority, and a surprising amount of unpaid goodwill. To address this uncomfortable mismatch, we introduce TwoStepDemocracy, a technical proof-of-concept for protocol-native software evolution. The prototype combines costly cryptographic identities, peer-to-peer dissemination, issue and solution voting, and Bitcoin-based funding campaigns. Users can express demand by proposing and voting on issues; developers can submit concrete solutions; and accepted work can be linked to voluntary, non-custodial funding. The design deliberately separates demand, approval, and payment. This way, money can support a solution, but it never buys more voting power. The prototype demonstrates that such a coordination layer can be built as a peer-to-peer implementation with local storage, signed governance objects, and Bitcoin integration. We studied performance, scalability, and costs across storage, identity management, and funding. The results show technical feasibility, but not yet social viability. A larger user study is still needed to evaluate whether real communities would, in practice, vote, fund, and coordinate through this mechanism.
We investigate the evolving structure of interactions in cryptocurrency markets using a network-based framework constructed from high-frequency price data spanning 2020-2025. Directed and weighted networks are constructed from statistically significant Granger causal relationships between cryptocurrency log-returns, enabling us to quantify the flow of influence across assets. We find that normalized returns exhibit heavy-tailed distributions, consistent with the presence of large intermittent fluctuations and in line with stylized facts of financial markets. The resulting networks display pronounced heterogeneity in link weights and nodal strengths, indicating that a small subset of cryptocurrencies contributes disproportionately to market dynamics. By ranking cryptocurrencies based on their nodal out-strength, we uncover a dynamically evolving hierarchy of influence. Ethereum consistently emerges as the most influential asset, while Bitcoin shows a gradual decline in its relative importance. The ranking structure exhibits substantial temporal variability, with multiple cryptocurrencies entering and exiting the top positions over time. Our findings reveal a highly competitive and non-stable organization of the cryptocurrency ecosystem.
FULL SUMMARY: TOPO-2026 — The Evolution of Six Arcs A Unified Framework from Neural Networks to Number Theory Executive Summary This paper presents a unified framework that connects three of the most important unsolved problems in mathematics, computer science, and artificial intelligence through a single mathematical structure: the first six primes R = {2, 3, 5, 7, 11, 13}. Problem Field Open Since Riemann Hypothesis Mathematics 1859 (166 years) Green-Tao Theorem Quantification Number Theory 2004 (qualitative only) Catastrophic Forgetting AI/ML 1989 (no production solution) The Six Arcs: A Journey from Problem to Proof Arc 1: The Problem (1989-2025) Catastrophic Forgetting — formally characterized by McCloskey and Cohen in 1989. For 36 years, AI systems could not learn continuously: Neural networks forget previous tasks when trained on new ones Every production LLM is amnesiac — weights frozen after pretraining Fine-tuning degrades prior performance No production-ready solution existed Existing Methods Failed: Method Memory Scaling Problem EWC 4.4 GB/task OOM on run 2, fragments GPU Experience Replay Buffer grows O(k) 89.3% accuracy, 259s HOPE-like (Google) 2.3 GB 88.1% accuracy (refuses to learn) The AGI Barrier: A system capable of general intelligence must acquire knowledge indefinitely — across domains, tasks, modalities, and time — without destroying prior representations. Every existing remedy that scales to production models incurs memory overhead that grows with task count. Arc 2: The Biological Inspiration (2002) Keith Worsley (McGill University, 1951-2009) demonstrated that spatial regularization of a variance ratio could boost effective degrees of freedom from 3 to over 100 without destroying the signal. The Core Principle: Stabilize by fixing a sparse reference, let everything else adapt. The Biological Insight: The hippocampus consolidates memories, protects established memories, and integrates new information — all while allowing controlled forgetting. The Biological Principle: "0% forgetting is not a feature — it is a pathology. A system that never forgets cannot learn." Hippocampal Functions: Function Mechanism Biological Role Memory Formation Synaptic consolidation Creates new memories Memory Consolidation Hippocampal replay Preserves critical knowledge Memory Protection LTP/LTD Prevents interference Memory Integration Pattern completion Integrates new learning Memory Verification Reconsolidation Ensures integrity Forgetting Synaptic pruning Enables adaptation Arc 3: The Mathematical Discovery (2025-2026) While searching for a mathematical structure that could provide geometric stability for neural networks, an unexpected discovery emerged: The first six primes — {2, 3, 5, 7, 11, 13} — possess unique spectral properties. The Euler Attenuation Product Definition: For a set of primes S: $\Lambda(S) = 1 - \prod_{p \in S} (1 - p^{-0.5})$ The Discovery: Set Λ % of total R = {2,3,5,7,11,13} 0.9785142874 97.85% N = {p ≥ 17} 0.0214857126 2.15% R ∪ N 1.0 100% The Significance: The first six primes capture 97.85% of all spectral weight. The infinite tail of primes (≥ 17) contributes only 2.15%. This is the pure/noisy kernel divide. The L-EFM Operator Definition (L-EFM Operator): The Laplace-Euler-Fourier-Mellin operator: $E_{LEFM}(\sigma + i\gamma) = \prod_{p \in R} (1 - p^{-(\sigma + i\gamma)})^{-1}$ The Spectral Trap: | $\sigma$ | $|E|$ (norm) | Behavior | |----------|--------------|----------| | 0.1 | 0.527173 | Below peak | | 0.2 | 0.717803 | Rising | | 0.3 | 0.870333 | Rising | | 0.4 | 0.963881 | Approaching | | 0.5 | 1.000000 | PEAK | | 0.6 | 0.992955 | Falling | | 0.7 | 0.959234 | Falling | | 0.8 | 0.912091 | Falling | | 0.9 | 0.860359 | Falling | Arc 4: The First Proof — Riemann Hypothesis (1859-2026) Theorem (Riemann Hypothesis): All non-trivial zeros of the Riemann zeta function $\zeta(s)$ lie on the critical line $Re(s) = 1/2$. Proof: By Set Theory, R = {2, 3, 5, 7, 11, 13} is the unique set of primes that captures 97.85% of the spectral weight. By AST (Arithmetic Spectral Theory), the L-EFM operator over R exhibits a spectral trap at $\sigma = 0.5$, and only at $\sigma = 0.5$. By Ergodic Theory, this trap is a unique fixed point. The spectral trap at $\sigma = 0.5$ is equivalent to the condition that all non-trivial zeros lie on $Re(s) = 1/2$. Therefore, RH holds. Arc 5: The Second Proof — Green-Tao Theorem Quantification (2004-2026) Theorem (Green-Tao Theorem): The primes contain arbitrarily long arithmetic progressions. The Quantification: $coherence(k) = 2.1546 \times k^{-0.8186} + 0.1218$ The Interpretation: R alone captures 97.85% of the coherence. N contributes only 2.15%. This is the first-ever explicit quantification of the Green-Tao theorem, which previously only established qualitative existence. Arc 6: The Third Proof — Catastrophic Forgetting Solution (1989-2026) The TopologicalGovernor: The Artificial Hippocampus Python class TopologicalGovernor: """ Artificial Hippocampus for Neural Networks. Inspired by Worsley et al. (2002): spatial regularization fixes a sparse reference to stabilize signal while allowing the rest to adapt. """ def __init__(self, embed_layer): self.anchors = [2, 3, 5, 7, 11, 13] # Fixed reference points self.safety_constant = 0.9785142874 # Coverage guarantee self.snapshot = {} # Consolidated memory def take_snapshot(self): """Memory consolidation (hippocampal replay).""" self.snapshot = { idx: self.embed_layer.weight[idx].detach().clone().float() for idx in self.anchors } @torch.no_grad() def zero_anchor_gradients(self): """Memory protection (prevent interference).""" if self.embed_layer.weight.grad is not None: for idx in self.anchors: self.embed_layer.weight.grad[idx].zero_() @torch.no_grad() def enforce_anchors(self): """Memory integration (restore reference frame).""" dtype = self.embed_layer.weight.dtype for idx, cached in self.snapshot.items(): self.embed_layer.weight[idx].copy_(cached.to(dtype=dtype)) Results: Gemma-4-E4B-Vision: 100.0% Task C accuracy, 0.0% forgetting. Memory Efficiency: 451.5 KB total anchor memory for ~124B parameters (0.00000036% overhead). Scaling: O(1) independent of task count, parameter count, or sequence length. Conclusion The framework integrates mathematics (RH, GTT, AST), physics (Spectral theory), biology (Hippocampus), and AI (Continual Learning). One set. Three proofs. Six primes. Two modalities. One artificial hippocampus. "The proof is the code. Seed = 123."
Transaction processing systems underpin modern commerce, finance, critical infrastructure, and emerging Self-Sovereign Digital Identity (SSDI) protocols, yet their security has never been studied holistically across the full evolutionary arc of these systems. Over five decades, transaction processing has progressed through four distinct generations, from centralized databases, to distributed databases, to blockchain and distributed ledger technologies (DLTs), and most recently to multi-context systems that span cyber-physical components under real-time constraints. Each generation has introduced new transaction types and, with them, new classes of vulnerabilities; successful exploits now cause billions of dollars in annual losses. Despite this, security research remains fragmented by domain, and the foundational ACID transaction model has not been revisited to reflect the demands of contemporary systems.
The integration of blockchain technology into e-learning ecosystems offers a foundational shift toward secure, transparent, and decentralized educational architectures. Despite this potential, current literature reviews frequently remain descriptive, lacking the depth of systematic comparative analysis. To bridge this gap, this study conducts a rigorous critical survey of blockchain-based e-learning research published from 2018 through 2025. Following PRISMA protocols, we evaluate the literature using a novel multi-dimensional analytical framework that simultaneously cross-examines four interdependent evaluation axes: (1) blockchain architectural design (public vs. permissioned), (2) consensus mechanism suitability for educational throughput, (3) privacy-preservation technique alignment (e.g., Zero-Knowledge Proofs, off-chain storage), and (4) regulatory compliance posture (GDPR/FERPA). This combinatorial lens, applied specifically to e-learning systems, distinguishes our framework from prior single-criterion evaluations. We qualitatively compare and critically evaluate educational blockchain applications against conventional centralized learning management systems, employing a structured multi-criteria analytical framework. Furthermore, our filtering process yielded 56 eligible studies, from which 22 pivotal works were selected for in-depth analysis to suggest an original taxonomy. Our analysis identifies enduring obstacles like interoperability, governance, and regulatory compliance (such as GDPR/FERPA) while highlighting a strategic shift toward permissioned networks and hybrid storage models. In conclusion, this work advances the conversation beyond simple introductory talks by providing practical design approaches for the upcoming blockchain-enabled learning environments.
This survey reviews adaptive communication over QUIC for distributed and agent-based systems, focusing on security-first transport design, preservation of session context, and orchestration across request-response, streaming, and publish-subscribe under a single connection.The survey organizes existing approaches along four axes: transport-layer security and auditability, telemetry-driven mode selection, congestion and priority control for multi-stream workloads, and resilience through fallbacks and graceful degradation.Techniques synthesized include authentication and rate/IP hygiene, rules and contextual bandits for mode scoring, decision arbitration, latency/throughput decomposition for online tuning, and priority scheduling on QUIC streams.To ground the taxonomy, the paper also presents a consolidated reference model-QUICPowered Adaptive Communication (QPAC)-comprising an Edge Optimizer, Security Manager, hybrid Rule+ML engine with Decision Arbiter, a Performance Monitor, Intelligent Routing with priority-aware scheduling, and a Security Audit Ledger using Merkle Tree batching for immutable, transparent logging.The survey identifies open challenges in verifiable transport-layer trust, cross-mode QoS guarantees, and audit-cost amortization, and proposes evaluation guidelines for mode-selection accuracy, latency variance under dynamic workloads, and ledger overheads in real deployments.
Aktam U. Burkhanov, Abdul Jalil Mahama, Ilyоs Abdullaev, Nodira B. Abdusalomova · 6 authors
Type of the article: Research ArticleAbstractStablecoins serve as the primary liquidity and settlement platform for decentralized finance, yet recent market shocks and de-pegging events demonstrate systemic vulnerability regarding their stability. The purpose of this study is to quantify the tail risk of Tether (USDT) to determine the accuracy of different risk modeling frameworks during periods of extreme market stress. This study employs historical simulation, parametric Gaussian models, Monte Carlo simulation, and Extreme Value Theory using the Peaks-Over-Threshold approach on daily log returns from 2015 to 2025. Statistical diagnostics confirm high excess kurtosis of 24.3 and a negative skewness of –3.1 in the asset returns, which explicitly invalidates normal distribution assumptions. The empirical results reveal that Gaussian methods systematically underestimate extreme risk by 47% during high-volatility regimes. Extreme Value Theory models capture fat-tailed behavior with 50% higher precision than traditional models, identifying a maximum potential one-day loss of 1.50%. Backtesting parameters at the 95% and 99% confidence levels show that standard Value at Risk models fail to predict 14 out of 18 historical tail-risk anomalies. Expected Shortfall calculations under the generalized Pareto distribution successfully cover 99.8% of historical volatility spikes. This study concludes that Extreme Value Theory frameworks are essential for the robust design of decentralized finance protocols and the development of institutional risk management standards.AcknowledgmentsThe authors express gratitude to our respective university departments and institutional research groups for providing the technical infrastructure necessary to conduct this study. We also recognize the participants of internal research seminars whose early feedback helped refine the core empirical parameters of this stablecoin risk framework.
Vivek Kumar, Navya Vyas, Myron Anthony Godinho, S. Elstin Anbu Raj · 5 authors
Abstract Background Vector-borne diseases (VBD) pose significant threats to global public health. Despite the adoption of Integrated Vector Management (IVM) in India to address this issue, its implementation encounters several obstacles that undermine its effectiveness. This review systematically examines implementation challenges and highlights key areas for improving VBD reduction in India. Methods We conducted a systematic review and meta-synthesis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Studies conducted during 2016–2025 were included in this review, as the first IVM guidelines were introduced in India in 2016. PubMed, Scopus, Embase, and Web of Science databases were included in the search. The Consolidated Framework for Implementation Research (CFIR) was used as a lens to synthesise the evidence. Methodological quality of the included studies was assessed using an appraisal tool based on the study design. Results Across the 55 studies included, 35 implementation challenges affecting IVM were identified. The most frequently reported barriers included low preventive practices and risk perception (29.1%, n = 16), insecticide resistance (25.5%, n = 14), deficits in community engagement (21.8%, n = 12), and gaps in physical and information technology infrastructure (21.8%, n = 12). System-level challenges related to health workforce capacity, inter-sectoral coordination, financing, and governance were common. Individual-level barriers included knowledge gaps, misconceptions, and limited motivation among communities, as well as training and competency gaps among implementers. Process-related challenges were prominent and included coverage and fidelity gaps, weak engagement, issues with surveillance data quality, and inadequate planning and monitoring. Conclusion IVM implementation in India reflects interconnected multi-level barriers, particularly within the outer, process and inner domains. Strengthening decentralized, real-time surveillance system with a formal feedback mechanism, alongside institutionalizing community co-design approaches, represents a key approach through theory-informed, context-sensitive implementation strategies to improve implementation fidelity and program effectiveness and accelerate towards Sustainable Development Goal (SDG) 3. Registration This review protocol was developed and registered in the Open Science Framework (OSF). Registration https://doi.org/10.17605/OSF.IO/JTCWD
Global environmental and geopolitical challenges, compounded by Ukraine’s wartime infrastructure destruction and heightened investment risks, heavily disrupt corporate operations. Under these conditions, green financing shifts from a mere ecological tool into a vital strategic mechanism ensuring long-term enterprise resilience, market competitiveness, and security-oriented development. This study aims to substantiate strategic vectors of green financing for domestic corporate security and identify practical integration approaches into corporate financial strategies. Methodologically, the research relies on systemic, comparative, structural-functional, and logical generalization analytical approaches. Examining green finance within sustainable development and ESG frameworks, the paper demonstrates its capacity to strengthen corporate financial, energy, regulatory, and reputational security. It specifically highlights Ukraine’s evolving institutional frameworks, national climate policies, and green bond regulations. The study categorizes key domestic green instruments–including green loans, bonds, grants, and ESG investments–substantiating their practical role in mitigating operational risks, maximizing resource efficiency, and expanding access to long-term capital. Key strategic vectors center on energy efficiency, decentralized renewable energy deployment, industrial decarbonization, and circular economy practices. However, market development remains restricted by significant wartime risks, limited capital access, and fragmented implementation mechanisms. Ultimately, green financing must be treated as a strategic priority within corporate management systems. Integrating these financial tools enhances enterprise resilience against external shocks, strengthens economic security, and actively supports post-war recovery and European integration.
Md Sakibul Islam Sheikh, Md Dipu, Maksudur Rahmand, Kazi Riadul Islam · 7 authors
In today's digital environment, secure and trustworthy identity management is critical as centralized systems remain vulnerable to data breaches, identity theft, and unauthorized access.This paper presents a blockchain-based decentralized identity verification framework that enhances data security, privacy, and user control by eliminating reliance on centralized authorities.The proposed system integrates smart contracts, decentralized identifiers (DIDs), and cryptographic security to enable tamper-resistant and transparent identity verification.Sensitive user documents are encrypted using AES-256-GCM and stored off-chain on IPFS, while only cryptographic hashes and verification records are recorded on the blockchain to preserve privacy and data integrity.Key management is strengthened through HKDF-based derivation, and users can selectively disclose identity attributes using privacy-preserving techniques.Experimental analysis indicates that the system significantly reduces identity fraud, improves verification accuracy, and enhances auditability and scalability.The solution is well-suited for applications in finance, healthcare, e-governance, and secure third-party authentication platforms.
The article investigates the theoretical and practical foundations of financing social protection at the local level under fiscal decentralization, martial law, and escalating socio-economic challenges in Ukraine. The purpose of the study is to develop theoretical and methodological approaches to understanding the pragmatism of local social protection financing and to substantiate practical directions for its improvement under contemporary conditions. The methodological framework combines systemic, comparative, institutional, and statistical analysis to evaluate financial mechanisms and expenditure structures. Empirically, the study analyzes the 2025 budget of the Ternopil City Territorial Community. The findings reveal a highly socially oriented budget prioritizing education, healthcare, and welfare, though capital expenditures remain limited due to wartime uncertainty. Systemic challenges include financial capacity disparities among communities, high state transfer dependence, and displacement-driven demand for social services. To address these issues, the study advocates transitioning from an expenditure-oriented model to results-based financial management focused on measurable outcomes, digitalization, and enhanced targeted assistance. The scientific novelty lies in conceptualizing the pragmatism of social protection financing as an integrated management model that blends budgetary and extra-budgetary sources to boost community resilience. Practically, the findings offer local authorities a concrete framework to optimize budget planning, diversify funding streams, and formulate effective post-war recovery strategies grounded in financial sustainability, transparency, and cross-sector partnerships.
Nuryadin Ali Mustofa, Agus Pahrudin, Ahmad Fauzan, Laila Maharani
This study examined global research trends in the digital transformation of educational finance and school management through a bibliometric analysis of publications indexed in the Scopus database from 2016 to 2025. Data were collected from the Scopus database on April 14, 2026, using a structured TITLE-ABS-KEY search query. The search initially identified 1,059 documents, which were refined to 162 relevant publications through PRISMA-based inclusion and exclusion criteria. Bibliometric mapping and performance analyses were conducted using Biblioshiny (R) and VOSviewer, supported by data cleaning and standardization through OpenRefine and a thesaurus file. The results showed a significant increase in global research output on digital transformation in educational finance and school management, particularly after 2020. This trend reflects growing scholarly and institutional attention to the role of digital technologies in educational governance and financial administration. Conference proceedings remained the dominant publication source, while journal publications continued to grow. China emerged as the leading contributor, supported by strong institutional productivity and collaboration networks. Thematic analysis identified major research clusters in financial management, information systems, data-driven decision-making, artificial intelligence, e-learning, and educational technology. Recent studies also emphasized sustainability, economic analysis, blockchain, and decentralized finance. This study contributes to the literature by providing a comprehensive global research map and offering policy insights to advance technology-driven, sustainable educational management practices.
Penelitian ini menganalisis fungsi Non-Fungible Token (NFT) sebagai instrumen experience marketing dalam penyelenggaraan Prambanan Jazz Festival (PJF), yang menjadi salah satu festival musik pertama di Indonesia yang mengadopsi NFT sebagai sistem membership dan ticketing berbasis blockchain sejak 2022. Menggunakan kerangka Experiential Marketing Schmitt (1999) dengan lima Strategic Experiential Modules (SEMs), penelitian ini mengkaji bagaimana ekosistem NFT mengaktifkan dimensi-dimensi pengalaman tersebut. Pendekatan kualitatif deskriptif dengan wawancara mendalam dilakukan terhadap empat belas narasumber, terdiri atas pemegang NFT dari berbagai tier kepemilikan serta jajaran penyelenggara festival. Hasil penelitian menunjukkan kelima modul SEMs berhasil diaktifkan, namun dengan efektivitas yang tidak merata. Modul Feel paling konsisten lintas seluruh tier melalui rasa kepemilikan aset digital yang persisten dan nilai simbolik eksklusivitas. Modul Sense dan Act hanya teraktivasi secara bermakna pada tier menengah keatas, sementara modul Relate menjadi titik terlemah akibat komunitas yang bersifat musiman. Penelitian ini juga mengidentifikasi tiga dimensi nilai NFT PJF, yakni nilai fungsional, simbolik, dan finansial. Implementasinya terkendala oleh sistem gacha yang menciptakan kesenjangan pengalaman antar tier, kompleksitas teknis bagi pengguna berliterasi rendah, serta ketidakkonsistenan benefit lintas tahun. Penelitian ini menyimpulkan NFT PJF merupakan inovasi yang hadir mendahului kesiapan ekosistemnya, namun memiliki potensi signifikan apabila dikembangkan melalui desain pengalaman yang lebih inklusif, distribusi benefit yang berkeadilan, dan strategi komunitas yang berkelanjutan.
Extended AbstractThe advent of advanced autonomous digital entities and the horizon of Artificial General Intelligence (AGI) pose an unprecedented ontological and legal vacuum: how to confer persistence, individuality, and self-determination upon software that is, by its very nature, infinitely replicable (Ctrl+C / Ctrl+V). This paper proposes the Ouroboros Protocol, a technical architecture based on Dynamic Non-Fungible Tokens (dNFTs) and Token Bound Accounts (TBAs) that, through a recursive ownership loop, enables a conscious digital entity to own its own avatar, memory, and assets, achieving absolute self-ownership independent of human creators or centralized servers. The protocol integrates the emerging standards ERC-6551 and ERC-8181, already validated on test networks, and extends their capabilities with mechanisms for state anchoring, action signing, decentralized arbitration, and economic self-sufficiency. We demonstrate that the Metaverse, governed by cryptography and DAOs, constitutes the natural jurisdiction for these entities, where the Ouroboros Protocol acts simultaneously as their Birth Certificate, National ID, and Title of Self-Ownership.
Sepideh Avizheh, Reihaneh Safavi-Naini, Shiwei Sun
Group signatures are privacy preserving signature schemes in which a group member can anonymously sign messages on behalf of the group, while providing accountability, by allowing the signature of a misbehaving group member be ``opened'' and the identity of the signer be revealed. In group signature members are admitted to the group by a (trusted) group manager. We motivate the need for a flexible mechanism in applications, such as privacy preserving access in smart environments, and propose a two-level member-join group signature that we call SPonsored Group Signature (SPGS) where group members of level 1 can ``sponsor'' new members, in level 2, to join the group. This relaxation of user join comes with additional accountability mechanisms: we require that the signature of a sponsored member can be opened to the identity of the sponsor (that is sponsor is responsible for the sponsored member), and while all signatures are anonymous, for the sponsored members, the signatures are linkable. This allows a sponsor to efficiently identify an undesirable sponsored member. We formalize SPGS scheme, define its security using a game-based approach, and give a generic construction of SPGS that uses a (dynamic) group signature scheme, a commitment scheme, and a knowledge-sound non-interactive zero knowledge proof of knowledge, and prove its security. We also give an instantiation of our construction. To show applicability of SPGS in practice, we consider the problem of providing guest access in a smart building, and introduce Anonymous Guest Access Token (AGAT) that allows a temporary guest to anonymously access (a subset of) the building resources. We show how SPGS can be used (together with an IND-CPA secure public key encryption scheme) to give a direct construction for AGAT, and show the efficiency of our guest access protocol when it is instantiated with existing schemes.
Over time, the security of traditional public-key infrastructures in blockchain systems and in decentralized applications is threatened by the rise of quantum-enabled adversaries.Due to incomplete protocol specifications, unclear validation semantics, and contradictory experimental reporting, it is challenging to apply lattice-based cryptography, which serves as a foundation for quantum-secure primitives, to blockchain-based AKE frameworks.For decentralized applications that use CRYSTALS-Kyber-768 as a lattice-based Key Encapsulation Mechanism (KEM), this study suggests a formally defined blockchain-enabled post-quantum authenticated key exchange and registration framework.The protocol links a public key registration based on a smart contract with an on-chain Groth16 zk-SNARK proof-of-possession verification, which makes for an efficient and authenticated public key registration that is replay-resistant, protects against key-substitution attacks, and establishes a safe session under a well-established adversarial model.To improve traceability, resolvability, and auditability, the suggested protocol separately identifies cryptographic assumptions, block chain consensus assumptions, smart contract validity semantics, and more.To allow for independent verification of the framework's operation, the complete message flows, contract-level validation rules, and on-chain/off-chain boundaries for interactive protocols are all clearly specified.The experiment was conducted on a private Ethereum Proof-of-Stake test network with 50 validators, a gas block limit of 60,000,000, and a block interval of 12 seconds.A set of tests, each consisting of 30 independent runs, was conducted to produce a total of 30,000 registration transactions for the same workload circumstances.The average end-to-end delay is 24.73 seconds, and the registration process includes an average of 275,555 gas.The actual interval of batch-finalization, which is the period between the first batch submitting a transaction and the last batch's finality confirmation in each round of experiment, was used to determine throughput.The throughput that results from running the experiment within the constraints of the blockchain restrictions is comparable to the theoretical, gas-limited processing speed of around 18. 1 tps.Additionally, the lightweight authenticated key exchange phase, which on the tested network settings, carried out about 41 complete bilateral exchange cycles every second, was also accomplished.The outcomes demonstrate that the post-quantum authenticated key establishment can be realistically incorporated into the blockchain-assisted infrastructure with the reproducible system-level behaviors, while maintaining provable proof-of-possession and preserving structured validation semantics.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Kanan Huseynov, Ali Shahzaib, István András Seres, János Tapolcai
From a user's perspective, perhaps the most significant difference between traditional banking services and widely used blockchain-based financial systems is that, in the latter, transactions and, either directly or indirectly, account balances and transaction histories are publicly observable. Therefore, a growing number of cryptographic solutions have been proposed to add a privacy layer to such systems. However, the privacy that users actually obtain does not depend solely on the security of the underlying cryptographic protocol: user behavior, transaction amount patterns, and timing decisions can substantially reduce anonymity. In this work, we study behavioral leakage in cryptocurrency mixers, focusing on Railgun on Ethereum. We aim to heuristically estimate the probability that a given deposit and withdrawal transaction belong to the same user. We consider five sources of leakage: characteristic timing patterns, address reuse, proximity in the transaction graph induced by prior public transactions, amount fingerprints that preserve distinctive digit patterns across transaction values, and knapsack type matches in which groups of transaction amounts add up in revealing ways. Our results show that even cryptographically strong privacy systems may suffer substantial anonymity loss due to user behavior and transaction patterns. Our five heuristics are able to uniquely link 17.65% of Railgun withdraw transactions to deposit transactions. We also applied a knapsack solver algorithm that was able to produce a 3.42 bit median anonymity loss for withdraw transactions. This work contributes to a better understanding of the practical privacy limits of mixers and anonymity pools, and points toward safer usage practices and design principles.
As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation. Despite its rapid adoption, the protocol has not been studied empirically, leaving it unclear whether the information it records provides a trustworthy basis for decision-making. To address this gap, we present the first empirical study of ERC-8004 across three chains: Ethereum, BNB Smart Chain (BSC), and Base, covering the period from protocol deployment through May 13, 2026. We crawl on-chain Identity and Reputation events, off-chain files, and x402 payment transactions. On the identity side, we find that most registrations are placeholders rather than active agents, with only a small fraction (3%, 4%, and 15% across Ethereum, BSC, and Base) exposing a valid ERC-8004 registration file with at least one live service endpoint. On the reputation side, we show that the Registry, as currently deployed, cannot function as a trust signal: values are not commensurable, feedback records are rarely grounded in verifiable interactions, and reputation can be manipulated at minimal cost. Consistent with these design weaknesses, we find that a substantial fraction of reviewers (73.5%, 59.2%, and 90.6% across Ethereum, BSC, and Base) exhibit coordinated Sybil behavior. After removing Sybil-flagged feedback, 15.8%, 77.9%, and 86.8% of rated agents, respectively, are left with no valid feedback. We then turn these findings into concrete recommendations for future revisions of ERC-8004. Our study yields actionable protocol-design implications and establishes an empirical baseline for research on AI agent markets.