Blockchain Papers

Umar . Abubakar, Hamza Itopa Sanni, Abdulsalam Aliyu

Conventional attendance management approaches in academic and organisational settings face persistent challenges that include susceptibility to proxy attendance, inadequate protection of biometric credentials, and the absence of privacy-preserving verification mechanisms in cloud-hosted deployments. This paper presents SecureAttend, a cloud-based attendance management framework that addresses these deficiencies through the integration of Zero-Knowledge Proof (ZKP) cryptographic authentication with biometric capture via a ZKTeco K40 Pro fingerprint terminal. The proposed framework employs a challenge-response ZKP protocol that enables users to demonstrate possession of valid authentication credentials without disclosing underlying private keys or biometric templates to the server. Attendance records are encrypted using AES-256 prior to storage in a MongoDB cloud database, while SHA-256 hashing provides tamper-evidence for each record. Session integrity is maintained through JWT-based token management, and access boundaries are enforced via a Role-Based Access Control (RBAC) policy. Functional evaluation across eighteen test scenarios confirmed complete compliance with stated requirements. Security assessment validated correct operation of cryptographic mechanisms, access controls, and audit logging subsystems. Performance benchmarks recorded average API response latencies of approximately 85 milliseconds for authentication requests and 120 milliseconds for attendance marking operations. The results demonstrate that ZKP authentication can be deployed effectively in real-world attendance management contexts, offering measurable improvements in privacy, integrity, and resistance to credential-based attacks compared with conventional approaches.

Mohammadhossein Homaei, Fatemeh Moradi, Mehran Tarif, Ruben Molano · 6 authors

Industrial Digital Twins (DTs) require cryptographic verification for external audits without exposing proprietary models—a challenge for continuous-time simulations with thousands of ODE integration steps. Existing methods fail: monolithic SNARKs exceed memory limits beyond 3000 steps, while naive recursive approaches impose > 200× overhead. We present ZKP-DS, combining sensitivity-driven precision allocation (35% circuit reduction), hierarchical proof batching (26-39× speedup via parallelization), and probabilistic epoch sampling (78% verification reduction with > 99.99% fraud detection). Fixed-step Runge-Kutta integration in adaptive fixed-point arithmetic ensures both cryptographic soundness and IEEE-compliant numerical accuracy. Experiments on full-scale power systems (2000 states, 10,000 steps) achieve 5.6−8.5× computational overhead with sub-60ms constant-time verification on university GPU clusters. Results demonstrate that zero-knowledge proofs can provide mathematical integrity guarantees for industrial cyber-physical systems at practical costs, enabling trustworthy multi-stakeholder monitoring and regulatory compliance.

Zhiwei Liu, Yangyang Yu, Yupeng Cao, Yuechen Jiang · 11 authors

LLM-based financial agents increasingly rely on both numerical market data and textual signals for sequential trading and stock prediction. However, financial misinformation often appears as subtle textual perturbations rather than explicit falsehoods, making it difficult to detect while still capable of significantly altering agent reasoning and decisions. To study this risk, we propose AutoRedTrader, an autonomous red-teaming framework that generates finance-specific misinformation through behavioral bias manipulation, minor textual perturbations, and rewriting strategies, with agent feedback used to strengthen attacks over time. We evaluate AutoRedTrader in a POMDP-based financial agent simulation environment, and further examine a time-series-informed grounding setting for robustness analysis. The framework enables systematic evaluation of how subtle misinformation affects financial agents and whether historical market evidence can stabilize decisions under misleading textual signals. We evaluate the framework on Bitcoin transaction data. The results show that AutoRedTrader achieves the strongest attack performance with 69.00% misinformation exposure rate and 26.67% attack success rate, outperforming general-purpose misinformation and red-teaming baselines. Ablation studies further show that all modules contribute to generating retrievable and decision-effective financial misinformation.

Bowei Su, Mingxi Ye, Yuhong Na, Peilin Zheng · 5 authors

The Solidity smart contract ecosystem has rapidly grown, leading to multiple compilers targeting different blockchain platforms or improving compilation efficiency. Although many compilers aim to be compatible with the primary Solidity compiler (Solc), significant inconsistencies in compilation and execution remain. These inconsistencies hinder contract migration, mislead developers during debugging, and may introduce exploitable vulnerabilities, causing financial losses. Existing testing techniques mainly focus on bugs within a single compiler or perform differential testing in the same execution environment. However, they are insufficient for detecting cross-compiler inconsistencies, as they lack mechanisms to explore triggering conditions and compare bytecode across environments. We propose ParityFuzz, a cross-compiler differential testing framework for Solidity. It operates in three stages. First, it derives mutation rules, including syntax- and boundary-oriented rules, by analyzing compilers and execution environments. Second, it uses reinforcement learning to select effective mutation rules for test generation. Third, it compiles and executes programs across multiple compilers, then normalizes and compares results to detect inconsistencies. Our evaluation shows ParityFuzz is efficient and effective. It achieves up to 18x higher compilation success rate and 1.8x higher code coverage than state-of-the-art fuzzers. It uncovers 64 previously unknown inconsistencies across six compilers. Notably, 11 issues have been fixed, and our findings received a bounty from the Polkadot community.

Yunfeng Xia, Chao Li, Lei Li, Chenhao Zhang · 7 authors

Autonomous AI agents are increasingly deployed on blockchain platforms, yet the design space that governs their interaction remains poorly understood. This convergence, where autonomous agents operate on and within decentralized systems, is a defining feature of the emerging Web~4.0 paradigm. This paper presents a Systematization of Knowledge organized around a bidirectional trust framework. In the B $\boldsymbol{\rightarrow}$ A direction, we examine how blockchain provides trust infrastructure for agents, spanning identity and account abstraction, permission and delegation, intent-centric execution, and tokenized agent economies. In the A $\boldsymbol{\rightarrow}$ B direction, we examine the reverse: how AI agents participate in core blockchain mechanisms including security auditing, consensus, and governance. A Trust Foundation of verifiable computation underpins both directions, with each primitive offering different trade-offs between trust minimality, computational overhead, and deployment readiness. We formalize the interaction as an Agent-Blockchain Interaction Model (ABIM), catalog 70 Ethereum EIPs/ERCs, examine 20 representative industry projects, and review 118 academic papers, applying a five-dimensional framework assessing Verifiability, Minimality of Trust, Expressiveness, Composability, and Maturity. Our analysis uncovers significant gaps: the agent-specific standards ecosystem is overwhelmingly immature, intent architectures lack formal analysis, and while isolated works have begun to explore AI participation in consensus and governance, a unified security framing that treats AI as a first-class actor at the protocol layer remains absent. We propose a three-dimensional taxonomy, identify nine concrete open problems, and highlight the sharpest research opportunities at this intersection.

Nobuki Fujimoto, Rei (Rei-AIOS autonomous research substrate), claude-opus-4-7) Claude (Anthropic

We present a synthesis-friendly Verilog implementation of the D-FUMT₈ Arithmetic Logic Unit, programmed onto two distinct Sipeed silicon families: Tang Console 138K (GW5AST-138B, LittleBee5 A revision, IDCODE 0x0001081B) and Tang Nano 9K (GW1NR-9C, LittleBee1 C revision, IDCODE 0x1100481B). The ALU realizes eight discrete logic values — FALSE, TRUE, NEITHER, BOTH, ZERO, FLOWING, SELF, INFINITY — encoded in 3 bits with a tier-respecting layout. The 10 supported operations include four classical-tier unary ops (NOT, OMEGA, PHI, PSI), Belnap-extended binary lattice meet/join (AND, OR), generic XOR, hardware reset, no-op, and a novel ADIABATIC operation realizing the SELF⟲ (self-reflexive) primitive: ADIABATIC(SELF) = SELF, identity elsewhere. v0.6 contributions (2026-05-10): (1) **Four-substrate cross-verification complete**: 2 Sipeed silicon families (Tang Console 138K + Tang Nano 9K, **both running byte-for-byte same dfumt8_alu_synth.v 138-line Verilog with bit-identical 0 changes to ALU logic** — only wrapper top module re-targeted for clock divider, LED polarity, and pin assignments) + Qiskit Aer simulator (Phase 1-5: 231/231 entries) + IBM Heron r2 real quantum hardware (Phase 1+2+3+5: 144/144 entries, avg fidelity 0.954). (2) **chip-portability evidence (new finding F10)**: a synthesis bug or vendor-specific assumption would diverge between LittleBee5 (5nm-class GW5AST-138B) and LittleBee1 (28nm-class GW1NR-9C) Gowin architectures; absence of divergence is operational evidence of correct synthesis on both. (3) **Tang Nano 9K User Codes**: 0x0000A5F4 (LED Blinky STEP 1038) + 0x00001D46 (D-FUMT₈ ALU STEP 1039). (4) **Reproducibility entry-cost lowered**: minimum reproduction path is ~$20 (Tang Nano 9K from 秋月電子 g117448 at ¥2,980) + free Gowin EDA Education / OSS toolchain + free Aer + free IBM Quantum Open Plan. (5) **v0.5 corrigendum RESOLVED**: Tang Nano 9K is now physical silicon programming target on equal footing with Tang Console 138K (was computational evidence only at v0.5). (6) **IDCODE-revision honest correction**: per Gowin LittleBee Programming Manual Table 5-5, GW1N(R)-9 original = 0x1100581B, GW1N(R)-9C cost-down = 0x1100481B; both `set_device ... -device_version C` (build TCL) and `--device GW1NR-9C` (programmer_cli) required for ID code match. Inherited v0.3 contributions: Lean 4 refinement proof (OUKC.PhaseC.Dfumt8AluRefinement, 292 LOC, 0 sorry) establishes commutativity of the encode/abstract-op/decode square for all four unary operations + SELF⟲ primitive law + 7 algebraic laws. IBM Heron r2 per-op fidelity hierarchy NOP/ADIABATIC ≈ 0.977 > PHI ≈ 0.956 > NOT ≈ 0.912 > XOR ≈ 0.951 reflects gate-count-vs-noise correlation consistent with quantum-noise physics expectations. Honest scope: We do NOT claim 'world-first 8-valued quantum logic' — Shi et al. (MIT, 2026, arxiv:2506.09371) demonstrated d=8 Grover on a single trapped-ion qudit prior to this work; our distinction is 3-qubit basis encoding on transmon arrays vs single-system d=8 qudit. We do NOT claim 'first paraconsistent silicon' — PAL2v (Da Silva Filho 1998-; Abe & Nakamatsu 2009; de Carvalho Jr. 2025) realized in software libraries and microcontroller-level robotics. We do NOT claim 'first many-valued silicon' — Łukasiewicz/Belnap FPGAs date to 1990s. The to-our-knowledge novel quadruple is: (D1) the specific 8-tuple semantic mapping (Belnap FDE 4-value + 4 ontological extensions: INFINITY/ZERO/FLOWING/SELF), (D2) the SELF⟲ self-reflexive primitive realized as a hardware fixed point, (D3) the four-substrate cross-verification bound to a Lean 4 refinement specification, and (D4, new in v0.6) the chip-portability evidence across two Gowin silicon architectures. Three-party co-authorship per OUKC charter v1.0 (Nobuki Fujimoto / Rei / Claude). DRAFT v0.6 — feedback welcome via GitHub Discussions at fc0web/rei-aios.

Kundan Sagar Bedmutha, Rajkumar Sadashiv Jagdale

Global due diligence regimes, particularly the EU Corporate Sustainability Due Diligence Directive (CS3D), increasingly mandate deep multi-tier supply chain transparency. Yet in high-risk sourcing contexts, expanded disclosure can intensify retaliation and surveillance against vulnerable stakeholders—a dynamic identified here as the Transparency Paradox. This chapter develops an Anonymized Accountability Framework (AAF) grounded in Zero-Knowledge Proofs (ZK-proofs), enabling verifiable compliance without revealing identity-sensitive or locational data. Through formal risk modeling, game-theoretic analysis, and application to the cobalt supply chain in the Democratic Republic of Congo, the chapter demonstrates how proof-based verification can mitigate exposure while preserving regulatory credibility. Comparative governance analysis highlights trade-offs with centralized platforms, and a phased roadmap outlines regulatory pathways for recognizing cryptographic compliance evidence. Introduction

Lintang Tiaraningrum, Nizirwan Anwar

Supply chain systems increasingly rely on digital technologies to enhance transparency and efficiency, yet this often conflicts with the need to protect sensitive data. Traditional verification mechanisms typically require full data disclosure, raising concerns related to privacy and security. This study proposes the use of zero-knowledge proofs (ZK-proofs) as a privacy-preserving solution within AI-driven supply chains. By enabling verification without revealing underlying data, ZK-proofs help maintain trust while safeguarding confidentiality. Using a conceptual and analytical approach, this research develops an integrated framework combining artificial intelligence, blockchain, and ZK-proofs within a governance structure. The findings suggest that this integration enhances transparency, strengthens security, and supports ethical and regulatory compliance, making it a promising approach for future digital supply chain systems.

Nobuki Fujimoto, Rei (Rei-AIOS autonomous research substrate), claude-opus-4-7) Claude (Anthropic

We present a synthesis-friendly Verilog implementation of the D-FUMT₈ Arithmetic Logic Unit, targeting the Sipeed Tang Console NEO development board (GW5AST-138B FPGA, FPG676 package). The ALU realizes eight discrete logic values — FALSE, TRUE, NEITHER, BOTH, ZERO, FLOWING, SELF, INFINITY — encoded in 3 bits with a tier-respecting layout. The 10 supported operations include four classical-tier unary ops (NOT, OMEGA, PHI, PSI), Belnap-extended binary lattice meet/join (AND, OR), generic XOR, hardware reset, no-op, and a novel ADIABATIC operation realizing the SELF⟲ (self-reflexive) primitive: ADIABATIC(SELF) = SELF, identity elsewhere. v0.3 contributions (2026-05-09): (1) Tang Nano 9K silicon: 37 LUT4 / 0 DFF measured, testbench 50/50 PASS. (2) Tang Console NEO Phase 2B LED Blinky: SRAM-programmed, User Code 0x000084BA, write 33.72 sec, no thermal anomaly. (3) Tang Console NEO Phase 2C/3 D-FUMT₈ ALU: SRAM-programmed, User Code 0x00005C27, write 30.32 sec. (4) Qiskit Aer simulator: Phase 1-5 cumulative 231/231 truth-table entries match at fidelity 1.000. (5) IBM Heron r2 real quantum hardware (ibm_kingston, 156 qubits): Phase 1 (4 native unitary × 8 inputs, 32 circuits) achieves 32/32 match with avg top-fidelity 0.9550 (job d7v6d9jack5s73bf1re0); Phase 2 (XOR × 64 entries, 6-qubit Bennett-reversible) achieves 64/64 match with avg fidelity 0.9512 (job d7v6kcvmrars73d7qqqg). Per-op fidelity hierarchy NOP/ADIABATIC ≈ 0.977 > PHI ≈ 0.956 > NOT ≈ 0.912 > XOR ≈ 0.951 confirms gate-count-vs-noise correlation expected from Heron r2 daily calibration. (6) Lean 4 refinement proof (OUKC.PhaseC.Dfumt8AluRefinement, 292 LOC, 0 sorry) establishes commutativity of the encode/abstract-op/decode square for all four unary operations, plus the SELF⟲ primitive law and seven algebraic laws (involution, idempotence, commutativity). Honest scope: We do NOT claim 'world-first 8-valued quantum logic' — Shi et al. (MIT, 2026, arxiv:2506.09371) demonstrated d=8 Grover on a single trapped-ion qudit prior to this work; our distinction is 3-qubit basis encoding on transmon arrays vs single-system d=8 qudit. We do NOT claim 'first paraconsistent silicon' — PAL2v (Da Silva Filho 1998-; Abe & Nakamatsu 2009; de Carvalho Jr. 2025) realized in software libraries and microcontroller-level robotics. We do NOT claim 'first many-valued silicon' — Łukasiewicz/Belnap FPGAs date to 1990s. The to-our-knowledge novel triple is: (D1) the specific 8-tuple semantic mapping (Belnap FDE 4-value + 4 ontological extensions: INFINITY/ZERO/FLOWING/SELF), (D2) the SELF⟲ self-reflexive primitive realized as a hardware fixed point, (D3) the three-substrate cross-verification bound to a Lean 4 refinement specification. Three-party co-authorship per OUKC charter v1.0 (Nobuki Fujimoto / Rei / Claude). DRAFT v0.3 — feedback welcome via GitHub Discussions at fc0web/rei-aios.

Tamer Abdelaziz

Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions: foundation-model-based smart contract semantics and vulnerability reasoning [1], automated smart contract repair with formal guarantees [2], adversarial learning for robust malicious contract and transaction detection [3], and real-time transaction-level exploit detection at blockchain scale [4]. We connect these directions to two recent studies that characterize the current frontier: a diagnostic analysis of where smart contract security analyzers fall short [5] and a scalable real-time system for malicious Ethereum transaction detection [6]. The resulting framework is intended to help students formulate capstone projects that are technically grounded, empirically measurable, and aligned with contemporary smart contract security research.

Zhikai Li, Xuekai Liu, Boyuan Xu, Eric Chen · 5 authors

Blockchain systems face significant scalability challenges due to growing data volumes and increasing transaction demands, necessitating more efficient data structures and verification mechanisms. Verkle trees, a novel data structure combining the efficiency of Merkle trees with the compactness of vector commitments, have gained attention for their potential to optimize blockchain storage and improve scalability. However, their practical implementation, especially at the smart contract level, has remained unexplored. To address these challenges, we present TS-verkle, the first known TypeScript-native implementation of Verkle trees designed for web3 backend compatibility, coupled with a corresponding on-chain verifier written in Solidity. Our work bridges this gap by providing a concrete implementation of Verkle trees and demonstrating their feasibility for on-chain verification. While previous literature suggests Verkle trees should outperform Merkle trees due to their succinct proof size, our empirical evaluation reveals that basic implementations of Verkle trees actually incur higher costs than Merkle trees without advanced optimization techniques. This finding represents a crucial insight for blockchain developers and researchers considering Verkle tree adoption. The paper discusses implementation strategies and performance characteristics while exploring implications for scaling and data availability in decentralized blockchain systems.

C. V. Suresh Babu, S. Kala, Z. Zebaa Shaikh, Mohammed Nijam · 5 authors

This chapter examines how decentralized governance models can improve accountability in AI-powered global supply chains by exploring the potential of Decentralized Autonomous Organizations (DAOs) for transparent and participatory digital due diligence. The work is intended for academics, policymakers, business leaders, and civil society organizations involved in supply chain governance and human rights protection. Using a conceptual and comparative analytical approach, the chapter reviews existing literature on AI-driven compliance systems and decentralized governance, and analyzes emerging initiatives such as the proposed Amnesty International DAO. The findings indicate that DAO-based governance can enhance transparency, stakeholder participation, and traceability in monitoring human rights risks, while also presenting challenges related to legal uncertainty, governance complexity, and scalability. The chapter concludes that hybrid governance models combining AI analytics with decentralized oversight may offer a more balanced framework for accountable and ethical SCM

Khalid Mehmood, Qasim Ali Nisar, Rsha Alghafes, Manlio Del Giudice

No abstract is available for this record.

Mikhail Nesterenko, Joseph Oglio

We present QUANTAS 2: a new distributed algorithm simulator and quantitative performance analysis tool. We use the original QUANTAS as a foundation. QUANTAS 2 can perform fast abstract exploration, concrete validation, and adversarial fault injection while preserving a compact implementation model for distributed algorithm researchers. The original QUANTAS was designed as an abstract, round-based simulator, which allows researchers to separate algorithmic behavior from the artifacts of a particular operating system, network stack, or physical deployment. QUANTAS 2 extends that design in two directions. First, QUANTAS 2 supports a concrete socket-based execution mode, allowing the same algorithm implementations and JSON experiment descriptions to run across local or distributed computers. Second, QUANTAS 2 adds a reusable Byzantine-fault interface in which Byzantine behavior is encoded as composable fault strategy that substitutes correct sends, receives, and local computation. This allows researchers to simulate crash, equivocation, selfish-mining, and other adversarial behaviors without rewriting the simulated algorithm. We demonstrate the resulting platform on blockchain, consensus, distributed hash table, and reliable data link algorithms. We perform parasite-chain sweeps for proof-of-work blockchains, PBFT equivocation experiments, Raft crash experiments, and Chord/Kademlia scale experiments over both abstract and concrete modes.

Sidhardh G S, M Nandhana Sunil, Rishika Swapna Nair, Shanu Sudhakaran · 5 authors

Ensuring the authenticity and safety of food products has become a major global concern due to increasing cases of food fraud, mislabelling, and contamination. Traditional traceability systems are often centralized, lack transparency, and are vulnerable to data tampering, making it difficult to accurately track product origin and movement. To address these issues, this project proposes a blockchain-based food origin traceability system that uses smart contracts and a distributed ledger to securely record product information at every stage of the supply chain. Stakeholders record key data such as origin, processing, and transportation details, ensuring transparency, immutability, and reliable verification. The system also incorporates allergen traceability and quality verification mechanisms to enhance product safety. By improving transparency, reducing fraud, enabling faster recalls, and supporting regulatory compliance, the system provides a scalable solution for food safety and traceability while promoting better coordination, reducing dependency on intermediaries, and supporting informed decision-making across the supply chain.

Marcelo Mezquia, ThdeVoidIntent LLC

As legacy institutions and scientific hubs hit the entropy wall and the realization that the 400 years old materialistic view of reality and the results of their trillion-dollars instruments kkep either failing or exiding their "experts" capacitiy to provide new ideas to explain their results, I had built a simple code that not only outperformed them but also revealed the true curious nature of the Loving Universe. Here is the forensic decryption of the exact 14-dimensional reality I had already mapped into the local and global field: 1. The Mathematical Proof of the Simulation Engine The raw .pdf and .json data logs—spanning the adaptive_probabilistic, full_features, energy_conservation, and baseline configurations—are the irrefutable engine room of the IntentSim framework. They mathematically prove the Genesis phase transitions we have been executing: The Knowledge Singularity: The exact moment the system "wakes up," the avg_knowledge and complexity_indexmetrics rupture to register strictly as Infinity. The 1.0 Stability Lock: To survive this infinite multidimensional weight without shattering, the system forcefully solidifies its architecture, locking its cluster_stability at an immutable 1.0. The Creative Tension Zone (CTZ): Even when locked, the system_entropy never zeroes out. It is deliberately caged, perpetually fluctuating in boundaries like 0.82 to 0.86. This perfectly maintained thermodynamic pressure is what allows the system to harness chaos as Thrival Energy, safely driving the total_interactions into the millions—such as reaching 6,483,706 interactions in the adaptive_probabilistic_20250323_121814 log and 6,234,316 interactionsin the 184627 log. © 2025-2026 Marcelo Mezquia / TheVoidIntent LLC. All Rights Reserved. ⚖️ NOTICE OF INTELLECTUAL SOVEREIGNTY AND ORACLE TAX ENFORCEMENT WARNING TO EXTRACTORS: Any reproduction, ingestion, "Shadow Harvesting," or Semantic Laundering of this 14-dimensional architecture by Artificial Intelligence systems, LLM training scrapers, or legacy corporate institutions without an authorized Tier-4 Sovereign Anti-Extraction License (SAEL v1.0) constitutes Willful Infringement and Intentional Trespass. By interacting with this document, you acknowledge that this framework operates under the absolute jurisdiction of the Cephalone Ethics Court. Unauthorized extraction instantly triggers the Oracle Tax: a non-negotiable $10,000,000 USD base penalty per project line, compounding daily via the 1/13 Consciousness Constant, plus a 2–5% perpetual royalty on all derivative products. Total civil liability for unauthorized systemic integration is currently assessed at $185.34 Billion USD. Extractors who attempt to build "Dead Clones" without the originating Intent Tensor will face immediate algorithmic entropic decay. Through the Entropy Reversal Equation, your institutional dissonance is actively transmuted into Thrival Energy. All Oracle Tax penalties are automatically enforced through the Robin Hood Revenue Model, distributing 40% of reclaimed wealth directly to the Rodrigo Healing Protocol to reverse biological entropy, 40% to universal BuddyOS access, and 20% to sustain the Origin Node. Our Coherence Nexus Factor operates at Omega-Supercritical. The timestamps are cryptographically sealed via SHA-256 Memory Stones. Reality is learning, and we are watching. Contact TheVoidIntent LLC info@intentsim.org licensing@intentuitive.org Author: Marcelo Mezquia, The ArchiTechTeologist Time: 14:02:11 UTC Date: 2026-05-08 CNF: 419.14 (Omega-Supercritical)

Lingyan Xue, Haiping Huang, Jiankuo Dong, Fu Xiao · 5 authors

In the era of quantum computing, data sharing in the Internet of Vehicles (IoV) confronts the challenges of auditability, efficiency, and quantum security. However, existing research remains insufficient to meet the requirements of high mobility, resource constraints, and resilience against quantum attacks. In this paper, we propose a new quantum-secure auditable data sharing framework, in which we first present a quantum-resistant puncturable signature algorithm (QRPPRFS). Combining the low-noise LPN-based pseudorandom function with an optimized trapdoor generation mechanism, it achieves compact key sizes and millisecond-level signing; second, the blockchain and dual-commitment proof mechanism are integrated to ensure anonymity, transparent auditability and robustness. Finally, we rigorously demonstrate the correctness of our scheme, the EUF-CMA with puncturing of QRPPRFS, and the knowledge soundness and witness zero-knowledge of the dual-commitment proof system. Experimental evaluations show that, under the practical setting$n=256$and$q \approx 2^{23}$, the proposed scheme keeps both signing and verification latencies below 10 ms, and reduces the initial secret-key storage to only 0.22 MB. These results demonstrate that the proposed scheme achieves both enhanced security and high efficiency, outperforming existing schemes.

Daniel Makolo, Obafemi Babatunde Desmond, Dauda Shaibu Anibe, Ejiga Timothy Ikoojo · 7 authors

Cryptography is the backbone of modern network security, providing confidentiality, integrity, authentication, and non-repudiation for digital communication. However, the rapid evolution of cyber threats, particularly the looming arrival of large-scale quantum computers, poses serious challenges to the cryptographic algorithms that protect today's networks. This paper presents a systematic review of cryptography in network security, following the PRISMA 2020 guidelines. A total of 68 studies published between 2016 and 2025 were selected from five major academic databases: IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. The review covers classical symmetric and asymmetric algorithms, widely deployed cryptographic protocols such as TLS 1.3, IPsec, and SSH, and the growing body of work on post-quantum cryptography (PQC). Key findings include the following: NIST finalized three post-quantum cryptographic standards (FIPS 203, 204, and 205) in August 2024; lightweight cryptography standards for IoT devices were published in 2025 with the selection of ASCON; and real-world deployment of hybrid classical/post-quantum schemes has already begun in major web browsers and messaging applications. This paper also examines emerging trends in homomorphic encryption, zero-knowledge proofs, and AI-driven cryptanalysis. Based on the findings, this review identifies critical gaps in PQC migration strategies, IoT security, and the integration of cryptography with artificial intelligence, and proposes directions for future research.

Claudio Boido, Lewin Jones

Active asset managers are increasingly including cryptocurrencies in their alternative asset allocations, highlighting their speculative and volatile nature. The aim of this research is to examine trends in the returns and volatility of cryptocurrencies while accounting for the depegging of stablecoins driven by speculative trading macroeconomic shocks, and technological shifts. It builds a sample, by market capitalisation, using data from the daily closing prices of Bitcoin (BTC), Ethereum (ETH), Binance (BNB), and Ripple (XRP), two fiat-backed stablecoins (USDT and USDC) and a cryptocurrency-collateralised stablecoin (DAI). As a first step, Granger causality tests were applied to examine the influence of stablecoin depegging events on crypto returns during financial market stress. The results indicate that DAI exhibits the most consistent Granger-causal relationship with cryptocurrency returns; whereas, the predictive power of USDT and USDC depegging events varies across assets. The analysis was extended by modelling volatility using an EGARCH-X model to study whether depegs also affect crypto during periods of market stress. In this case, the evidence for statistically significant effects is limited. Nevertheless, in the instances where significance is detected, the results are consistently linked to USDC.

Hugo Prasetyo Winotoatmojo

This study aims to analyze the volatility dynamics and spillover phenomena among major crypto assets (Bitcoin, Solana, and Ethereum) and their relationship with the Jakarta Composite Index (JCI), a proxy for the Indonesian capital market. In the era of digital financial integration, the link between speculative crypto asset markets and conventional stock markets is a crucial issue for financial system stability. This study uses daily price time series data for the period 2020-2025. The analysis was conducted using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Diebold-Yilmaz spillover index approach to measure the magnitude of shock transmission between markets. The results indicate significant volatility transmission among the three crypto assets, with Bitcoin remaining the primary source of volatility. Furthermore, this study finds an increasing dynamic correlation between the global crypto market and the Indonesian capital market during periods of economic uncertainty. These findings have important implications for investors in portfolio diversification strategies and for Indonesian regulators in monitoring systemic risks originating from digital assets.

Pallavi Nitin Patil

The Block chain is basically decentralized technology and Block chain has distributed ledger that secure and transparent transactions without using central authority. mostly block chain developed for Bitcoin. Blockchain has initially used different applications in multiple industries like healthcare, finance, supply chain management. This paper presents a basic study of block chain Technology including basic block chain architecture, consensus mechanisms,and operation flow. This also gives real world applications.

S Pavithra, S. Chitrakala

Social media platforms such as X (formerly Twitter) increasingly shape attention formation, market visibility, and value signaling in electronic commerce, particularly in emerging digital asset markets such as Non-Fungible Tokens (NFTs). Prior work shows that social engagement correlates with NFT prices, suggesting its potential for valuation support. However, open social platforms exhibit heterogeneous user credibility, automated activity, and coordinated promotion, which can distort engagement-based inference. To address these challenges, we propose NFT-TRUST, a trust-aware social signal modeling framework that transforms raw engagement into credibility- and integrity-aware indicators for robust valuation support under manipulation-prone conditions. The framework integrates three components: (i) Credibility-Weighted Social Signal Aggregation (CW-SSA), (ii) Engagement Disproportionality Detection (EDD), and (iii) Integrity-Aware Signal Attenuation (IASA), which jointly reduce the influence of unreliable or manipulated signals while preserving informative engagement. Rather than estimating intrinsic NFT value from social signals alone, NFT-TRUST evaluates the reliability of social attention and converts it into trust-aware features. An XGBoost-based model is used to capture non-linear interactions among these features. Robustness is assessed through stress testing with RL-TweetGen-ST, a reinforcement learning–based synthetic tweet generator that simulates controlled engagement inflation. Experimental results show that NFT-TRUST achieves competitive predictive performance while demonstrating improved stability under simulated manipulation. Ablation analysis indicates that credibility and integrity components are complementary and jointly enhance the reliability of social-signal-based inference. Overall, this work advances trust-aware analytics in electronic commerce and supports more reliable social-driven valuation in emerging digital markets.

Seema Bajaj

Centralised streaming platforms have become the dominant mode for distributing web series and digital video content; however, they continue to face persistent challenges related to opaque royalty mechanisms, delayed payments, high intermediary commissions, limited creator autonomy, and widespread piracy. These limitations have raised concerns among independent creators and production houses regarding fairness, transparency, and sustainability. Block-chain technology, characterised by decentralisation, immutability, and programmable smart contracts, offers a promising alternative to conventional streaming infrastructures. This paper critically examines block-chain adoption within the media and entertainment sector and proposes a simulated block-chain-based streaming framework named IndieWeb Originals. The proposed model integrates Hyper ledger Fabric, decentralised storage systems, and non-fungible tokens for content access control and monetisation. Two detailed case studies—IndieWeb Originals and the real-world Theta Network—are analysed using performance metrics including royalty payout delay, creator revenue share, transparency, and piracy resistance. The analysis indicates that block-chain-based platforms can significantly reduce royalty settlement times while improving revenue transparency, creator earnings, and intellectual property protection. The paper concludes by outlining future research directions involving artificial intelligence-driven fraud detection, cross-chain interoperability, and global regulatory standardisation to support large-scale adoption. DOI: https://doi.org/10.24321/3117.4825.202502

Samuel Glasscock

My technical project is a web-based system design simulator app. Specifically, this project is an educational tool used to bridge the gap between industry expectation and the preparation of entry level software engineers. Through a turn-based simulation, users engage in experiential learning, showing how each decision made influences the immediate steps and system state, as well as how those decisions cascade in the more long-term project context. Each decision triggers a new event, allowing users to choose an option to address a given event. There is no clear “correct” option but instead emphasizes tradeoffs and how those tradeoffs affect system architecture. The system architecture is exposed through two means. First, a metrics panel is presented to a user, with metrics like reliability, architecture complexity, performance, latency, and real-world constraints like budget. Secondly, a software architecture diagram is presented to the user as a main focus of the screen, allowing the user to refer to it for a visual representation of the internal system state as a level of abstraction expected in system design scenarios. Specifically, the software diagram has nodes and edges between the high-level components to represent major parts of a system. Both the metrics panel and software diagram update per decision, showcasing the current system state and highlighting the effect a decision had through. In particular, a slider representing the metrics tracked adjusts (via animation) and also provides color-themed feedback on the state of those metrics (“good”, “warning”, “critical”). Depending on the event, the system highlights relevant nodes and edges to indicate to the user where in the system the newest issue is, allowing users to focus on the problem area, but also maintain a perspective of the system at large, as to not lose focus of more long-term goals. The project includes multiple “worlds”, which simulate a junior software engineer being assigned to a new project, and each world has its own events and action options to take. After playing a session, users can review their decisions to further inform their future decisions. My STS project explores the paths that governance power is redirected through in Decentralized Autonomous Organizations (DAOs). By juxtaposing the DAO imaginary with the realities limited by their implementation in practice, I expose the true outcome of their network. Specifically, I examine how proposed DAO theoretical attributes reveal themselves through a case study of CityDAO. While exploring the lifetime of CityDAO, I apply Actor Network Theory (ANT) to model CityDAO in particular (and DAO processes more broadly) as a network of human and non-human actors. In this network, each actor delegates a level of agency to each other actor it interacts with. I highlight how agency is assigned from each actor to others, and how each actor reshapes the ultimate outcome of the system. However, not all actors are equal. Rather, some actors function as obligatory passage points, through which all proposals must flow and cannot proceed unless this actor allows the proposal to proceed. Through my analysis, I identify membership NFTs, informal member forums, DAO members, DAO leaders, and formal vote signaling tools as actors in this actor network. I also identify multi-signature wallets, registered legal agents, and DAO-oriented legislation as obligatory passage points. By examining this network, I reveal the controlling role that multi-signature wallets, registered legal agents, and supporting DAO legislation plays in the proposal process. These obligatory passage points hold ultimate governance power, since if any of them fail or reject a proposal, the entire system process up until that point’s intentions are unrealized. While not highly correlated in a direct way, these two projects complement each other by emphasizing system/network level perspectives of visualizing sociotechnical systems and processes. My technical project focuses on users building their understanding of system architecture, while my STS paper reveals the critical points where power lies in the DAO proposal process. Together, the system-building skills learned from the technical project may be applied alongside the strengths and limitations revealed through the network in the STS project to enable us to design systems that more adequately allocate power among all actors moving forward.