Blockchain Papers

Sharvina Srivastava

No abstract is available for this record.

Mateusz Grochowski

No abstract is available for this record.

Edward Kipkalya

The impending arrival of superintelligent AI systems poses an unprecedented challenge to human institutions: how can governance structures that oversee self-improving agents remain aligned with evolving human values when those agents will rapidly and irreversibly surpass their regulators in capability? This paper introduces Recursive Meta-Governance (RMG), a formal framework that embeds self-stabilizing, provably aligned meta-level institutions capable of governing lower-level systems—including AI agents—through endogenous recursion. Drawing on mechanism design, category theory, typed lambda calculus, and the scalable oversight literature, we define a recursive language for governance protocols, establish a minimal axiom system, and prove key properties: stability, alignment preservation under bounded capability growth, compositional modularity, and non-corruptibility under adversarial coalition pressure. We demonstrate applicability through lightweight formal simulations (freely executable Python pseudocode) and four conceptual case studies: the EU AI Act (Regulation (EU) 2024/1689), the NIST AI Risk Management Framework, corporate board governance, and the failure modes of decentralized autonomous organizations. Unlike static external oversight models, RMG creates an adaptive, self-correcting governance layer that co-evolves with the systems it regulates, guided at every step by formally verified alignment invariants. This work establishes the foundational theory for a new field we term recursive institutional engineering, offering a mathematically grounded pathway to safe long-term human flourishing amid transformative AI. All analysis is conducted with zero-budget tools (public literature, free Google Colab pseudocode, Overleaf/LATEX), making it fully replicable by any independent researcher.

Wulf A. Kaal

No abstract is available for this record.

Lev Goukassian

This paper presents the complete architectural blueprint for the Ternary Logic (TL) Smart Contract Constitutional Suite, defining the structural layout across three layers: the Logic Layer housing the ternary decision engine, the Execution Layer enforcing state transitions, and the Storage Layer providing immutable audit infrastructure. The blueprint specifies the precise components, interactions, and logic required to implement the unique triadic state model of the TL framework: Proceed (+1), Epistemic Hold (0), and Refuse (1). The Epistemic Hold state is introduced as a constitutional pause mechanism, transforming deliberation from an operational liability into a cryptographically verifiable evidentiary asset. The fail-closed default posture ensures that any transaction whose evidence has not been archived returns State 0, making uncertainty constitutionally visible rather than operationally invisible. The No Log = No Action invariant G(execute implies P(escrow_recorded and auditable)) is enforced across five independent layers from API schema validation through the on-chain terminal gate in TL_Ledger_Core.registerPermissionToken. The Dual-Lane Latency Architecture establishes a 2ms WCET hard ceiling for the Inference Lane and a 300ms hard ceiling for the Governance Lane, with the execution gate releasing only after a valid PermissionToken has been registered on-chain. The blueprint covers Solidity implementation patterns, a TLA+ formal verification specification proving the Epistemic Hold safety and liveness properties, an Oracle-Custodian asynchronous callback architecture, and the Ghost Governance prevention mechanism ensuring no contract call is made without a valid PermissionToken from the Governance Lane. Use cases are demonstrated across Central Bank Digital Currencies, decentralized finance, supply chain management, and AI-driven decentralized autonomous organizations, establishing TL smart contracts as constitutional code where the rules of economic interaction are harder to break than traditional legal agreements.

Tatiana Arden

No abstract is available for this record.

Massimo Franceschet, Enrico Bozzo

No abstract is available for this record.

RIYAD SHIKDER

No abstract is available for this record.

Mijian Wang

No abstract is available for this record.

Ravinjeet Singh

No abstract is available for this record.

June Ma

The dissertation studies how privacy and trust are shaped by digital technologies: how individuals value privacy over personal data, how AI alters trust and disclosure, and how decentralised blockchains can sustainably replace trusted intermediaries. Chapter 1 argues that the 'privacy paradox' --- that individuals claim to value privacy, yet readily disclose personal data --- arises because privacy is treated as monolithic, when it is multidimensional. I develop a framework that distinguishes voluntary disclosure from involuntary data diffusion, reconciling the paradox by showing that disclosures reflect contextual trade-offs. Using a discrete choice experiment, I provide estimates of privacy valuations across both institutional and social contexts. I find that privacy has substantial value when exposure results in harmful consequences, such as socially revealing data reaching close contacts. I also document an AI privacy puzzle: individuals are less concerned about privacy from AI assistants than from the firms that develop them. Chapter 2 examines this AI privacy puzzle. Using a survey experiment, I replicate the finding from Chapter 1 specifically for firms in the AI industry, highlighting the privacy gap that arises despite the clear product--firm relationship. An information treatment that explicitly links AI assistants to their firms increases concern about both, but does not significantly reduce this gap. Instead, the gap also reflects the anthropomorphic features of AI assistants, aversion to the commercial nature of firms, and the trust and perceived control consumers attach to each. However, when respondents evaluate real-world AI assistant--firm pairs, brand familiarity is the strongest predictor of where privacy concern is attributed. Chapter 3 considers decentralised trust in blockchain systems, in which consensus mechanisms replace trusted intermediaries. I propose a 'proof of quiet quitting' consensus mechanism that reduces the excessive energy consumption of proof of work while retaining the decentralisation that proof of stake can compromise. By introducing a participation lottery with unrestricted entry and an endogenous cutoff, the mechanism separates maximum effort capacity from the probability of winning, inducing participants to exert no more than the minimum effort required in equilibrium.

T Krishna, Vadlakonda Sai Vishal, J. Vamsinath

ABSTRACT Efficient disaster response requires scalable, transparent and trustworthy resource management systems. However, centralized approaches frequently suffer from coordination delays, data tampering risks, limited transparency and single points of failure, reducing reliability during large‐scale crises. This study presents a decentralised blockchain‐based framework that integrates smart contracts, decentralised multi‐source oracles for Internet of Things (IoT)‐enabled field reporting, role‐based access control and adaptive urgency scoring to improve allocation prioritisation and trust calibration. The architecture follows a structured three‐tier design. The edge layer supports real‐time sensing and secure data offloading through the Inter‐Planetary File System (IPFS). The blockchain logic layer enforces operational policies, dynamic prioritisation, and reputation scoring using modular, gas‐efficient smart contracts. An integration/API layer ensures secure interoperability among emergency agencies and stakeholders. A hybrid blockchain model combines Ethereum Proof‐of‐Stake (PoS) for public transparency with a permissioned consortium chain for controlled governance. Natural Language Processing (NLP) derives urgency scores from textual disaster reports, while a dynamic supply‐demand aware algorithm adapts resource allocation in real time. Multi‐signature governance and reputation mechanisms further enhance accountability. Experimental evaluation on a simulated testnet demonstrates throughput up to 1000 Transactions Per Second (TPS), alongside measurable improvements in fairness, auditability and allocation efficiency.

Akshara Alagarsamy, Naveenbalaji Gowthaman

DharmaCoin is an artificial intelligence-based cyber fraud detection system that is supposed to establish a safe and moral online financial system. The suggested model enables combining blockchain security, fraud detection with the use of artificial intelligence, biometric multi-factor authentication, and secure document verification to enhance the safeguarding of digital financial crimes. It has a framework built on the values of Indian philosophies based on logic, transparency, and ethical governance and in the name of responsible and trust-worthy digital finance. DharmaCoin manages to identify transaction fraud with a 97% F1-score and forged/altering documents with 99% accuracy in using state-of-the-art technologies SatyaAI, a real-time deepfake and identity-checking tool, and RishiGuard, an anomaly financial activity detecting tool, in 200 milliseconds. The system uses a Proof-of-Stake blockchain registry to ensure unalterable records of transactions, and to increase the level of transparency within financial deals. Performance checks show that the blockchain infrastructure has the capability of handling a rate of transactions of over 1000 transactions in less than five minutes as well as be able to verify transactions in less than five seconds hence scaling to high financial volume environments. A Pilot project involving a banking partner found that a reduction of up to 40 in time to process KYC manually led to a faster approach to operations and better fraud detection. In general, DharmaCoin offers a safe, transparent, sustainable digital finance system, which is a moral and tech-centered solution to the prevention of cyber fraud and promotes the trustful financial ecosystem in accordance with the global sustainability development objectives.

Patrick Woitschig, Ruting Wang, Wolfgang Karl Härdle

No abstract is available for this record.

Samson Ojo

No abstract is available for this record.

C. Ramya, A. Suphalakshmi

The increasing complexity of cyber threats across IoT-cloud infrastructures necessitates the use of innovative, flexible, and confidentiality-preserving prevention techniques. The Blockchain-Assisted Hybrid Attention-Based Intrusion Detection and Access Control System (BHA-IDACS) is presented in this paper. The primary detection module employs an Adaptive Spatio-Temporal Representation Architecture-Self-Attention and Intersample Attention Transformer (Astra-SAINT) to precisely detect evolving intrusion tendencies. A heron optimization algorithm (HOA) is utilized for tuning the model thereby improving accuracy of detection and convergence. Fully Homomorphic Encryption (FHE) maintains the security of data and storage of encrypted data in unsecured cloud and blockchain circumstances. On a Consortium Blockchain, all encrypted transactions and audit trails are maintained by a Proof-of-Stake Authority (PoSA) consensus method. Additionally, based on user behavior and trust level, Smart Contract-Based Dynamic Access Control independently enforces permission and authentication regulations. The suggested model provides better precision, recall, F1-score, F2-score, specificity, and Cohen's Kappa values in addition to a mean accuracy of 99.16%. Furthermore, statistical analysis using confidence intervals and low standard deviation values demonstrates that Astra-SAINT is reliable and consistent across all validation folds. These results demonstrate the efficacy of the suggested Astra-SAINT framework as a scalable and dependable intrusion detection method for protecting IoT environments of the next decade.

Hexiao LI, Jaafar Gaber, Salah Laghrouche

No abstract is available for this record.

R. Bala, S. Gnanavel

The exponential growth of cloud computing has enabled large-scale data outsourcing but has simultaneously introduced critical challenges related to data confidentiality, integrity, and trust. Traditional cryptographic and blockchain-based cloud security solutions often suffer from high computational overhead, latency, and scalability limitations, which hinder their practical adoption. To address these issues, this study proposes a robust and lightweight blockchain-based security framework for secure cloud data storage. The framework integrates hybrid AES–ECC encryption, smart contract–driven access control, and a lightweight consensus mechanism combining Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve efficient and tamper-resistant data management. The proposed system employs an on-chain/off-chain hybrid architecture that stores only essential metadata and cryptographic proofs on the blockchain while maintaining the actual data in distributed cloud storage. This design minimizes computational burden and blockchain bloat while ensuring end-to-end transparency and verifiability. A Merkle tree–based Proof of Storage (PoS) mechanism enables rapid integrity verification without requiring full data retrieval. Comprehensive experiments were conducted using a simulated multi-node cloud environment to evaluate encryption efficiency, transaction latency, throughput, storage overhead, and energy consumption. Results show that the proposed framework outperforms existing blockchain-based models, achieving a 37.7% reduction in encryption/decryption time, a 51.3% decrease in transaction latency, and a 54.5% improvement in energy efficiency. Additionally, the system attained a 99.3% security success rate under various attack scenarios, demonstrating its resilience against unauthorized access, replay, and tampering attempts. These findings confirm that the proposed approach provides a practical balance between security assurance and performance optimization.

John Manuel Barrios, Christoph Bertsch, Linda Schilling

No abstract is available for this record.

Viktoriya A. Evseenko

Subject. This article discusses the peculiarities of forming a holistic approach to assessing human capital as an economic resource of regions to improve the efficiency of its use in the context of decentralization. Objectives. The article aims to study human capital as a factor in the economic growth of regions in the context of decentralization, identify interregional disparities in its development, and substantiate strategic areas for intensifying the formation, funding, and effective use of human potential at the territorial level. Methods. For the study, I used analysis and synthesis, induction and deduction, abstraction, comparison, generalization, and systems and dialectical methods. Results. The article proposes strategic areas for intensifying and financing the development of human capital, taking into account modern challenges, and it pays particular attention to the institutional capacities of regions. The article substantiates current approaches to financing human capital in the context of the transition to a decentralization model, as well as in the systematization of data on investments in human resources for 20232025, and it formulates proposals for improving the sustainability of regional development through the optimization of inter-budgetary transfers, taking into account the existing interregional imbalances in the Human Development Index. Relevance. The results obtained have a certain practical value, as they can help regions more effectively utilize their potential, attract investment, create new jobs, and develop their own unique strategies for human capital development based on territorial characteristics.

Irene Aldridge

No abstract is available for this record.

Toshisada Utsunomiya

No abstract is available for this record.

Cynda Jones Carswell

No abstract is available for this record.

Steven Paul Nohr

Blockchain-based financial systems increasingly intersect with regulated domains, including stablecoins, real-world asset (RWA) tokenization, decentralized finance (DeFi), decentralized autonomous organizations (DAOs), and ESG-linked financial instruments. Existing blockchain insurance and underwriting models rely predominantly on probabilistic risk pricing derived from historical data, oracle-fed inputs, and machine learning inference. While sufficient for limited-scale applications, these approaches exhibit structural limitations when applied to high-volume, regulation-intensive systems. This paper demonstrates that probabilistic risk pricing alone imposes a fundamental scalability ceiling, as residual risk grows unbounded with system volume. We introduce a control-oriented risk mitigation framework based on the Crystal Validator (CV), which enforces execution-level compliance constraints prior to transaction finalization. By reducing compliance entropy through deterministic validation, CV bounds residual risk independently of transaction volume. We formalize this distinction using control theory, information theory, and cyber-physical systems (CPS) principles, and show why improved machine learning alone cannot resolve these limitations. The results establish control-oriented validation as a necessary architectural primitive for sustainable blockchain insurance and regulated on-chain finance.