Blockchain Papers

Ravitheja Papareddy

No abstract is available for this record.

Ranjeeth Kashetty

No abstract is available for this record.

Neha Mundhada

Blockchain technology, in simple words, is an innovative force that democratizes the methodologies of financial transactions by creating safe, traceable, and unalterable digital data. This research investigates how blockchain increases financial transparency in banking, government, and supply chain management for different sectors. It identifies block chain’s core features: decentralized ledgers, real-time auditing, and transparent data sharing, in total reducing information asymmetry and thus fraud, increasing public trust. This study will focus on the role of block chain in financial reporting, as immutable transaction records ensure audit-free error-free error checks and compliance with regulatory standards. The primary use cases for this are anticorruption government procurement systems, banking networks improving fraud detection, and supply chain platforms ensuring product traceability. Smart contracts integrated into financial processes help reduce intermediaries and promote accountability. The paper concludes with an overview of emerging trends in zero-knowledge proofs, decentralized finance, and blockchain-based governance systems that may transform the standards of transparency. Some policy recommendations for leaders are investment in blockchain research, the development of regulatory frameworks, and fostering cross-industry collaboration. Blockchain technology is expected to redefine financial transparency through accountability, fraud reduction, and increased public trust in digital economies.

Xiaoming Liu

This study focuses on the core needs of consumer data privacy protection in the context of the digital economy and creates a blockchain-based privacy and security architecture. Through a layered design, this architecture effectively combines data collection, blockchain core, privacy computing, smart contracts, and application integration modules. It integrates key techniques such as zero-knowledge proofs, homomorphic encryption, and decentralized identity to ensure that data is encrypted and stored throughout its creation and destruction, implements meticulous access rights management, and implements a verifiable audit process. The dataset used in this experiment is the 2024 CMS market county-level administrative district public dataset in the United States. In an environment simulating actual business pressures, the privacy protection effectiveness, system scalability, and computational and storage costs of this proposed system are tested. Comparisons are made with two typical implementations. While ensuring differential privacy and k-anonymity, the proposed system improves data transmission speed, reduces processing latency, and reduces storage consumption. This demonstrates the potential and superior performance of this system across multiple entities and industries. This study provides a practical and feasible technical implementation for blockchain-driven consumer data privacy protection and offers a verifiable engineering reference for data governance and cross-industry data sharing in the United States.

Yan Watequlis Syaifudin, Vipkas Al Hadid Firdaus, Imam Fahrur Rozi, Chandrasena Setiadi · 8 authors

The digitization of health records has enhanced clinical efficiency, but amplified risks related to data privacy, integrity, and auditability.While permissioned blockchains offer immutability and traceability, they often fail to reconcile transparency with confidentiality-either exposing sensitive data or obscuring it beyond regulatory scrutiny.To address this gap, this paper presents an integrated framework that combines Zero-Knowledge Proofs (ZKPs) with a permissioned blockchain to enable verifiable yet private healthcare transactions.A visit centric Electronic Health Record (EHR) model supports three real-world use cases: medication validity, procedure confirmation, and demographic verification.A four-layer architecture decouples data, application logic, cryptographic trust, and audit logging, allowing end-to-end validation without raw data disclosure.Experimental evaluation across three ZKP libraries (snarkJS, ZoKrates, and gnark) on a synthetic dataset of 1,000 patient visits demonstrates sub-500 ms verification latency, with snarkJS selected for its ecosystem compatibility despite slower raw performance.End-to-end pipeline latency averages 1.35 s, confirming feasibility for batch workflows such as insurance claims.The system further includes a web-based auditor interface that validates tamper-evidence under off-chain attacks, bridging cryptographic guarantees with operational compliance.

Sajjad H. Shah, Ian Walker, Mike Borowczak

Federated learning across IoT devices must simultaneously protect each device’s update from disclosure, prevent malicious participants from biasing the global model, and hide which devices are participating from outside observers. Existing systems typically address only a subset of these goals: secure aggregation hides individual updates but cannot validate them, plaintext-based robust filtering requires the server to see updates, and most cryptographic pipelines ignore timing privacy. This paper presents TriSAFE, a protocol composition for IoT federated learning with a single coordinating server and three threshold helpers. The server holds no decryption key. TriSAFE combines four mechanisms that are usually studied in isolation: (i) encrypted client updates accompanied by zero-knowledge proofs that each coordinate lies within a bounded range; (ii) a new lightweight binding step (the plaintext-equivalence protocol, PEP) that cryptographically ties the values proven in zero knowledge to the exact ciphertext later aggregated by the server, closing a substitution gap left by range proofs alone; (iii) helper-added differential privacy noise applied homomorphically before any decryption, so the server only ever sees a noised aggregate; and (iv) fixed-cadence batching with calibrated cover traffic to hide participation from passive network observers. Across two IoT intrusion-detection benchmarks (Edge-IIoTset and N-BaIoT) and MNIST, TriSAFE keeps accuracy within 0.1-2.1 percentage points of the no-attack baseline under Byzantine, label-flip, FANG, and time-delay attacks, with attack success rate below 1% (<0.1% for FANG). Timing inference by a passive observer drops close to chance, and the end to end overhead is 7-36% relative to a non-defended baseline. On MNIST, TriSAFE achieves 89-91% accuracy, 15-17 points above the MODEL benchmark under the same attack suite. The design is practical for gateway-assisted IoT deployments under the assumption that the coordinator does not collude with two helpers and that at least two helpers contribute honest DP noise.

Sweta Kahurke, Harsha Jain, Shifra Sheikh, Srushti Pillare · 5 authors

Decentralized finance (DeFi) platforms have experienced a rapid increase in fraudulent activities such as price manipulation, wash trading, and anomalous transaction behavior, while traditional centralized fraud detection methods remain unsuitable due to privacy and regulatory constraints. This study proposes a privacy-preserving fraud detection framework using federated learning, enabling multiple decentralized entities to collaboratively train a machine learning model without sharing raw transaction data. A real-world decentralized exchange (DEX) dataset containing over 100,000 transactions is preprocessed and enhanced through feature engineering techniques capturing swap rate deviations, transaction volume anomalies, and temporal patterns. In the absence of labeled fraud data, a heuristic-based labeling approach is employed to simulate realistic fraud scenarios. A Logistic Regression model is trained across multiple distributed client nodes, with model parameters aggregated using the Federated Averaging (FedAvg) algorithm over several communication rounds. The experimental findings show that the federated model delivers results similar to centralized methods while preserving data privacy, proving it to be an efficient solution for secure and scalable fraud detection in decentralized financial environments.

Andreas Park

No abstract is available for this record.

Alimul Ghani

No abstract is available for this record.

Alistair Milne

No abstract is available for this record.

Richard Hanna Beainy, Cesar Kamel

No abstract is available for this record.

Fernando Mori

No abstract is available for this record.

xuezhi cheng

Abstract Grounded in a hard-core perspective at the intersection of thermodynamics and information theory, this paper strips away the symbolic illusions of traditional political economy, redefining human society as an active, viscoelastic "colloidal fluid" dissipative structure driven by external energy throughput and the fundamental "time ledgers" of carbon-based individuals. Moving beyond monetary and financial fictions, the framework establishes the Dual Constraint Equations of Civilizational Survival: The Upper Limit Constraint Equation, which maps the physiological and physical time limits imposed on carbon-based components by the combined demands of innovative labor, unmechanized repetitive labor, and exponential administrative/game-theoretic complexity overhead ; and The Lower Limit Maintenance Equation, which dictates the non-negotiable minimum energy baseline required to prevent system disintegration. Using this apparatus, the work diagnoses two terminal pathologies plaguing modern advanced societies: Mechanization Blockade Disease (where stalled technological evolution forces human flesh to brute-force unmechanized repetitive labor, triggering population cliffs) and Complexity Parasitism (where skyrocketing administrative and defensive friction devours society's scarce innovative brainpower). The classical scholar Gu Yanwu once distinguished between the "fall of a state" (wangguo) and the "collapse of all-under-heaven" (wang tianxia)—the former representing the mere succession of ruling algorithms, and the latter the disintegration of a civilization's high-order negative entropy system. Examining the present, the global sphere is deeply mired in the quagmire of AI algorithm bubbles and capital centralization; from Asia, Africa, and Latin America to Europe and the Americas, state will is reasserting comprehensive control over physical resources. History and the laws of physics indicate that even though technological bubbles will ultimately burst, and even though centralized control will cause internal friction and complexity overhead to expand exponentially, we must still hope that this represents merely a transient system damping in the long river of history. For the Second Law of Thermodynamics has long delivered its ultimate verdict: without free physical sampling and distributed trial-and-error, human civilization will inevitably descend into absolute information dead silence. This theory may well transcend its era. Humanity remains trapped in the illusions of gold and fiat currency vouchers, unable as yet to embrace a true "Entropy Standard." Yet, a day will come when civilization is anchored upon the physical ledger of entropy-based accounting, and humanity will no longer rely on crude "combustion and explosions" to enact its achievements, but will instead drive grand phase transitions via minute energy leaps. So long as civilization's informational genes are not swallowed by entropy increase, the myth will endure across the starry seas of the universe.

Oyelokiki George Egbedayo

No abstract is available for this record.

Nikita Tiwari, Pradeep Kumar Biswal, Prakash Ranjan

The rapid development of the Internet of Medical Things (IoMT) has also facilitated real-time monitoring of healthcare, yet creates major issues of security, privacy, and interoperability, particularly in terms of emerging threats of quantum computing. This paper introduces a quantum-resilient blockchain paradigm, which combines post-quantum cryptography (PQC), Zero Knowledge Proofs (ZKPs), and Fast Healthcare Interoperability Resources (FHIR) into secure and interoperable healthcare data management. Key encapsulation is performed using lattice-based algorithms, including Kyber and NTRU, and Dilithium and Falcon are algorithms used to secure digital signatures against quantum attacks. An authentication system which is based on a ZKP, and role-based access control allows privatizing access to electronic health records without exposing sensitive data. The framework is deployed on a PBFT-based permissioned blockchain and tested in simulated IoMT settings and has low latency, high throughput, and efficient cryptographic performance. In general, the suggested system will provide a reconfigurable, secure, and future-oriented method to safeguard the healthcare information against quantum threats without compromising the interoperability of the heterogeneous systems.

Mohammad Raeini

No abstract is available for this record.

Tyler Nicewarner, Ali Allami, Dan Lin

Ensuring efficient task assignment and secure payment in mobile crowdsensing while preserving worker location privacy remains a challenging problem. Existing solutions either rely on expensive encryption schemes, employ blockchain-based verification that incurs high computational and gas costs, or use differential privacy techniques that degrade spatial accuracy. This paper introduces the Privacy-preserving Task Assignment and Payment (PTAP) framework, a lightweight solution built upon secure multi-party computation (SMPC). PTAP employs additive secret sharing and a challenge-response mechanism across three semi-honest servers to achieve anonymous task allocation and payment without blockchain or zero-knowledge proofs. The framework guarantees full unlinkability between worker identities, task locations, and payment records while maintaining accurate location-based assignment and supporting traceability for dispute resolution. Experimental evaluation using the MP-SPDZ framework demonstrates scalability to over 1.5 million workers and 7 million payment tokens. The average end-to-end completion time is approximately 35.4 seconds, with zero gas cost. Compared to the state-of-the-art AVeCQ system [15], which requires about 13 minutes and 37 MWei per transaction on the Goerli network for only 1,024 users. The results confirm PTAP's efficiency, scalability, and strong privacy guarantees for large-scale mobile crowdsensing deployments.

Emmanuel Gobet, Louis Latournerie

Decentralized Finance (DeFi) lending and borrowing protocols enable investors to take leveraged long and short positions on digital assets without centralized intermediaries, but expose them to a distinctive form of risk: on-chain liquidation triggered by debt and collateral value fluctuations. In this work, we provide a detailed formalization of Aave's lending, borrowing, and liquidation mechanisms, grounded in the protocol's open-source implementation. In doing so, we propose a mathematical modeling of the risk of liquidation, including some stochastic approximations with the purpose of efficient analysis, with different applications. Among them, portfolio optimization problem.

uthman Kamorudeen

This whitepaper introduces a new scientific framework for risk analysis in decentralized finance (DeFi) and cryptocurrency systems through the integration of artificial intelligence and robotic technologies. The research proposes an autonomous risk intelligence architecture that combines machine learning prediction models with robotic execution agents for real-time monitoring and mitigation of systemic financial risks. Key risk domains including market volatility, smart contract vulnerabilities, liquidity instability, oracle manipulation, governance threats, and regulatory uncertainty are formally classified and mapped to AI-driven detection and response mechanisms. The paper further introduces the Autonomous Risk Response Protocol (ARRP), a novel model for automated intervention using smart contracts, positioning AI-driven robotics as a foundational layer for next-generation decentralized financial security infrastructure.

Steven Paul Nohr

<b><i>Bribe-Based Oracle Echoes</i></b> describe a class of incentive-layer attacks in which economically rational adversaries influence oracle participants to repeatedly reinforce distorted data outputs without directly compromising oracle infrastructure. Unlike overt oracle manipulation, these attacks exploit incentive alignment and tolerance-band logic to create feedback loops in which biased signals are echoed, amplified, and normalized across decentralized finance (DeFi) and stablecoin systems. This paper formalizes the threat model, identifies structural enablers, and demonstrates why decentralization alone is insufficient to prevent economically induced oracle drift.

Derek Maurice

No abstract is available for this record.

Theocharis Grigoriadis

Why does industrialization in some cases generate social consolidation and in other cases political conflict? This paper argues that the answer depends on how industrial finance is allocated. I develop a dynamic political-economy model in which the government channels external liquidity into industry under either centralized or decentralized finance. Under decentralization, adverse shocks harden budget constraints and permit replacement of inefficient incumbents by new entrepreneurs. Under centralization, by contrast, the government is more likely to refinance inefficient incumbents, soften budget constraints, and block entry. Industrialization then generates concentrated rents and a higher risk of conflict. I interpret late imperial Russia as a historically revealing case of this mechanism. Rather than treating Russia as the sole object of interest, the paper uses it to motivate a general theory of industrialization under monopoly.

Mariam H.Eram

No abstract is available for this record.

Ambati Satya Sai Vaishnavi, M. Veera kumari, K. Akash Sai, G. Pavan Kiran · 7 authors

Peer-to-peer (P2P) energy trading has emerged as an innovative solution to modern energy challenges by enabling decentralized electricity exchange among users. The Small-scale market allows prosumers to sell excess energy directly to consumers without relying on centralized authorities. Blockchain ensures transparency, security, and immutability of transactions, while smart contracts automate trading operations based on predefined conditions. A MATLAB-based simulation environment is developed to model energy generation, consumption, and transaction processes, along with a digital ledger for recording trades. The results of different case studies demonstrate efficient energy utilization, reduced transaction costs, and improved reliability. The system promotes renewable energy adoption and supports the transition toward decentralized smart grids. This work highlights the feasibility of integrating blockchain technology with energy systems for sustainable and scalable power trading solutions.