The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these halvings, then the neoclassical economic theory posits that the price of Bitcoin should have increased due to the halving. But did it, in fact, increase for that reason, or is this a post hoc fallacy? This paper uses synthetic control to construct a weighted Bitcoin that is different from its counterpart in one aspect - it did not undergo halving. Comparing the price trajectory of the actual and the simulated Bitcoins, I find evidence of a positive effect of the 2024 Bitcoin halving on its price three months later. The magnitude of this effect is one fifth of the total percentage change in the price of Bitcoin during the study period - from April 2, 2023, to July 21, 2024 (17 months). The second part of the study fails to obtain a statistically significant and robust causal estimate of the effect of the 2020 Bitcoin halving on Bitcoin's price. This is the first paper analyzing the effect of halving causally, building on the existing body of correlational research.
Bhabendu Kumar Mohanta, Ali Ismail Awad, Tarek Elsaka, Hamza Kheddar · 5 authors
Intelligent devices with embedded technology have proliferated dramatically over the past decade. The Internet of Things (IoT) has emerged as a transformational force, advancing traditional systems to previously unattainable levels of intelligence. Smart cities, transportation, healthcare, supply-chain management, agriculture, water management, and smart grid (SG) systems are among the industries where the IoT has found applications. These developments are demonstrated by the integration of IoT systems into SG networks, offering significant improvements in sustainability, dependability, and efficiency. Such systems use various IoT devices to continuously monitor the environment and transmit data for processing and analysis. Nonetheless, the growth of the IoT has introduced security vulnerabilities, including concerns about user identification, data integrity, and trust, especially in SG applications. This study aims to resolve several security challenges in IoT-enabled SG applications to support sustainability. The proposed scheme effectively tackles critical security requirements such as data integrity, user anonymity, distributed storage, trust management, and decentralized architecture. The security concerns addressed by blockchain technology include preserving data integrity, fostering trust, providing secure communication, and enabling effective monitoring. Smart contracts automate system processes and are effective in maintaining user trust. The experimental findings support the viability of the proposed system, demonstrating a computational cost of 3.150 ms and a communication overhead of 992 bits, both representing improvements over various existing solutions. Additionally, the deployment cost for the smart contract is found to be 5.64 USD with a writing cost of 2.89 USD, both of which are lower than the costs associated with comparable approaches.
Rabia Arshad, Muhammad Milhan Afzal Khan, Saman Rasheed, Irtaza Ijaz · 5 authors
Blockchain technology has transformed decentralized data exchange and digital payments but the consistently high gas prices pose a significant challenge to its scalability and efficiency. This research explores the role of AI-driven gas price prediction and data compression methods on gas utilization in blockchain systems with special emphasis on Ethereum transactions. Using actual Ethereum transaction history, we compare the performance of compressed versus uncompressed payloads with three different compression algorithms: Zlib, Brotli, and Gzip. Beyond that, a linear regression model is also trained to forecast hourly gas Price fluctuations given past transaction history. The methodology includes thorough statistical analysis to provide accurate and reproducible results. Our results show that compressing text data over 141 bytes using the Zlib algorithm prior to making transactions on the Ethereum network decreases the amount of gas Used without altering system time. This validates the efficiency of combining data compression with gas price forecasting in minimizing transaction costs without affecting performance. Moreover, our study further encompasses investigation of actual gas Price trends and provides real-world insights for optimizing timing strategies for economic transaction execution. These results enhance the knowledge of Ethereum gas dynamics and provide valuable solutions for enhancing economic efficiency and resource utilization in applications based on blockchain. Future efforts will involve applying the framework to the Ethereum mainnet, using deep learning models for increased prediction accuracy, and adaptive compression dependent on network state and transaction size.
CoW Protocol batch auctions aggregate user intents and rely on solvers to find optimal execution paths that maximize user surplus across heterogeneous automated market makers (AMMs) under stringent auction deadlines. Deterministic single-objective heuristics that optimize only expected output frequently fail to exploit split-flow opportunities across multiple parallel paths and to internalize gas, slippage, and execution risk constraints in a unified search. We apply evolutionary multi-objective optimization to this blockchain routing problem, proposing a hybrid genetic algorithm (GA) architecture for real-time solver optimization that combines a production-grade, multi-objective NSGA-II engine with adaptive instance profiling and deterministic baselines. Our core engine encodes variable-length path sets with continuous split ratios and evolves candidate route-and-volume allocations under a Pareto objective vector F = (user surplus, -gas, -slippage, -risk), enabling principled trade-offs and anytime operation within the auction deadline. An adaptive controller selects between GA and a deterministic dual-decomposition optimizer with Bellman-Ford based negative-cycle detection, with a guarantee to never underperform the baseline. The open-source system integrates six protection layers and passes 8/8 tests, validating safety and correctness. In a 14-stratum benchmark (30 seeds each), the hybrid approach yields absolute user-surplus gains of approximately 0.40-9.82 ETH on small-to-medium orders, while large high-fragmentation orders are unprofitable across gas regimes. Convergence occurs in about 0.5 s median (soft capped at 1.0 s) within a 2-second limit. We are not aware of an openly documented multi-objective GA with end-to-end safety for real-time DEX routing.
Oct 24, 2025·Proceedings of the 2025 7th Conference on Blockchain Research and Applications for Innovative Networks and Services (BRAINS), 2025, pp. 1-4
This student paper introduces a novel methodology for the detection and analysis of multihop cross-chain arbitrage opportunities, wherein multihop denotes arbitrage sequences involving more than two transactional steps across distinct blockchain networks, executed using sequence-dependent strategies. Utilizing a comprehensive dataset comprising over 2.4 billion transactions recorded between September 2023 and August 2024 (encompassing 12 blockchain platforms and 45 cross-chain bridges) we design and implement an algorithm capable of identifying, sequence-dependent arbitrage paths spanning multiple ecosystems. Our empirical analysis demonstrates that such arbitrage opportunities are exceedingly infrequent, underscoring the inherent challenges associated with multihop execution in cross-chain environments.
Smart contract vulnerabilities cost billions of dollars annually, yet existing automated analysis tools fail to generate deployable defenses. We present FLAMES, a novel automated approach that synthesizes executable runtime guards as Solidity "require" statements to harden smart contracts against exploits. Unlike prior work that relies on vulnerability labels, symbolic analysis, or natural language specifications, FLAMES employs domain-adapted large language models trained through fill-in-the-middle supervised fine-tuning on real-world invariants extracted from 514,506 verified contracts. Our extensive evaluation across three dimensions demonstrates FLAMES's effectiveness: (1) Compilation: FLAMES achieves 96.7% compilability for synthesized invariant (2) Semantic Quality: on a curated test set of 5,000 challenging invariants, FLAMES produces exact or semantically equivalent matches to ground truth in 44.5% of cases; (3) Exploit Mitigation: FLAMES prevents 22 out of 108 real exploits (20.4%) while preserving contract functionality, and (4) FLAMES successfully blocks the real-world APEMAGA incident by synthesizing a pre-condition that mitigates the attack. FLAMES establishes that domain-adapted LLMs can automatically generate production-ready security defenses for smart contracts without requiring vulnerability detection, formal specifications, or human intervention. We release our code, model weights, datasets, and evaluation infrastructure to enable reproducible research in this critical domain.
This paper presents an option pricing model that incorporates clustered jumps using a bivariate Hawkes process. The process captures both self- and cross-excitation of positive and negative jumps, enabling the model to generate return dynamics with asymmetric, time-varying skewness and to produce positive or negative implied volatility skews. This feature is especially relevant for assets such as cryptocurrencies, so-called ``meme'' stocks, G-7 currencies, and certain commodities, where implied volatility skews may change sign depending on prevailing sentiment. We introduce two additional parameters, namely the positive and negative jump premia, to model the market risk preferences for positive and negative jumps, inferred from options data. This enables the model to flexibly match observed skew dynamics. Using Bitcoin (BTC) options, we empirically demonstrate how inferred jump risk premia exhibit predictive power for both the cost of carry in BTC futures and the performance of delta-hedged option strategies.
Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockchain ledgers threatens user privacy by enabling reidentification attacks through attribute analysis, while the computational complexity of policy matching clashes with blockchain's performance constraints. Existing solutions, such as those employing Zero-Knowledge Proofs (ZKPs), often incur high overhead and lack measurable anonymity guarantees, while efficiency optimizations frequently ignore privacy implications. To address these dual challenges, this paper proposes QAEBAC (Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute). QAE-BAC introduces a formal (r, t)-anonymity model to dynamically quantify the re-identification risk of users based on their access attributes and history. Furthermore, it features an Entropy-Weighted Path Tree (EWPT) that optimizes policy structure based on realtime anonymity metrics, drastically reducing policy matching complexity. Implemented and evaluated on Hyperledger Fabric, QAE-BAC demonstrates a superior balance between privacy and performance. Experimental results show that it effectively mitigates re-identification risks and outperforms state-of-the-art baselines, achieving up to an 11x improvement in throughput and an 87% reduction in latency, proving its practicality for privacy-sensitive decentralized applications.
Agostino Capponi, Alfio Gliozzo, Chunghyun Han, Junkyu Lee
This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance. Using more than 3K proposals from major protocols, we build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position. The agent operates within a realistic financial simulation environment grounded in verifiable blockchain data, implemented through a modular composable program (MCP) workflow that defines data flow and tool usage via Agentics framework. We evaluate how closely the agent's decisions align with the human and token-weighted outcomes, uncovering strong alignments measured by carefully designed evaluation metrics. Our findings demonstrate that agentic AI can augment collective decision-making by producing interpretable, auditable, and empirically grounded signals in realistic DAO governance settings. The study contributes to the design of explainable and economically rigorous AI agents for decentralized financial systems.
Aiming at the problem that smart contract security vulnerability detection faces high false positives in static analysis and low efficiency in dynamic analysis, which leads to low accuracy of vulnerability detection, a smart contract vulnerability detection method based on improved graph neural network (EGN, Event-Enhanced GNN) was proposed. Firstly, security mode features and graph features were extracted. Security mode features reduced false positives caused by blind detection, and graph features avoided the performance bottleneck of full graph traversal. Secondly, the high-risk functions were screened based on the risk probability threshold to improve the overall analysis efficiency. Thirdly, the temporal graph neural network was deployed for high-risk functions, the event temporal graph was dynamically tracked and the self-attention mechanism was used to capture vulnerabilities, so as to enhance the detection ability of complex vulnerabilities. Finally, we focus on reentrant vulnerability and timestamp dependency vulnerability detection. Through the evaluation experiments on the real contract datasets of two platforms of Ethereum and VNT chain, the experimental results show that the accuracy and F1 value of the proposed model for detecting reentries vulnerability reach 93.12% and 94.29% respectively, and the accuracy and F1 value of timestamp dependency vulnerability reach 91.71% and 91.42% respectively, which are better than the existing methods.
Abstract This study outlines the creation, implementation, and assessment of a Blockchain–AI integrated chain-of-custody (CoC) framework for managing digital forensic evidence. The research sought to improve the integrity of evidence, transparency, and automation, tackling the shortcomings of conventional manual CoC processes. The suggested system was executed utilizing Hyperledger Fabric (6 nodes, PBFT consensus) and Ethereum testnet (10 nodes, PoA consensus), attaining an average block time of 1.2–3.8 seconds and transaction latency of 85–150 milliseconds. Smart contracts, RegisterEvidence(), VerifyCustody(), AccessGrant(), and LogActivity() streamlined the custody procedure, achieving a 99.6% integrity validation rate in blockchain-only mode and a complete 100% validation when paired with AI anomaly detection. The AI subsystem utilized a CNN–LSTM combined model that was trained on 500 labeled transaction logs, achieving 97.2% accuracy, 0.96 precision, 0.97 recall, and an F1-score of 0.965. Correlation analysis indicated a robust positive association (r = 0.94) between AI anomaly detection and blockchain integrity verification. Scalability evaluations over 100–5,000 transactions demonstrated throughput between 135 and 80 transactions per second (TPS), while memory usage rose from 32% to 77%, verifying effective resource utilization. The system exhibited strong alignment with SDG 16 (Peace, Justice, and Strong Institutions) and SDG 9 (Industry, Innovation, and Infrastructure), achieving scores of 0.98 for transparency, 0.95 for accountability, 0.96 for innovation, and 0.94 for digital infrastructure. Comparative benchmarks indicated significant enhancements compared to baseline CoC systems: +13.1% in integrity validation, + 60.7% decrease in latency, + 97.2% increase in accuracy, and + 50% improvement in scalability. These empirical findings confirm that the Blockchain–AI framework provides a secure, transparent, and smart forensic environment, capable of revolutionizing judicial evidence handling and enhancing institutional trust via automated, data-driven processes.
Blockchain technology is rapidly becoming one of the most groundbreaking technologies for revolutionizing supply chain management with unprecedented security, transparency, and efficiency. This paper presents a comprehensive literature review of blockchain and its applications in leading industries such as transportation, manufacturing, food and beverage, and healthcare. Blockchain applies distributed ledger technology to secure tamper-evident record-keeping, which significantly enhances traceability and provenance verification across complex supply chains. By integrating smart contracts, IoT connectivity, and decentralized financial services, blockchain can solve significant challenges, such as counterfeiting, supplier management, and enforcing sustainable and responsible sourcing practices. Despite these benefits, the mass-scale adoption of blockchain faces serious challenges, such as scalability, interoperability, regulatory ambiguity, and a lack of standardized frameworks. The report also addresses the environmental concerns of blockchain’s power-intensive proof-of-work algorithm and discusses ways to counteract them. Future developments in artificial intelligence and 5G networks will continue to evolve supply chain management in ways that unleash unmatched efficiency and potential.
Driven by the increasing demand for multi-party data computation, Private Set Intersection (PSI) has become a pivotal technique for secure data sharing and privacy preservation. Although several efficient two-party PSI protocols have been developed, multi-party scenarios continue to suffer from limited computational efficiency and inadequate security guarantees. To address this engineering challenge, this study aims to enhance the performance and security of multi-party PSI protocols. We introduce SM-MPSI, a multi-party PSI protocol built upon national cryptographic standards. This protocol integrates SM2 and SM3 cryptographic mechanisms, employs non-interactive zero-knowledge proofs for identity authentication, and leverages domestic secure cryptographic chips to accelerate core algorithms. Experimental comparisons with existing mainstream protocols demonstrate significant improvements in computational efficiency and system scalability, while preserving robust security guarantees. Furthermore, SM-MPSI achieves enhanced communication efficiency and reduced resource consumption in multi-party scenarios. This research offers technical contributions toward advancing China's efforts in independent innovation in privacy-preserving computing and cryptographic technologies, thereby laying a solid foundation for strengthening national cybersecurity capabilities.
Brain–Computer Interfaces (BCIs) represent a transformative paradigm in human–machine interaction, enabling direct communication between neural signals and external devices. They hold immense promise in domains such as medical neuroprosthetics, defense communication, and immersive gaming. However, the neural data they process is highly sensitive, and current BCI frameworks that rely on centralized servers and traditional encryption are vulnerable to data breaches, manipulation, and the emerging threats of quantum decryption. These limitations highlight the urgent need for secure, privacy-preserving, and resilient architectures for BCI communication. To address these challenges, this paper introduces NeuroGuard, a blockchain-based framework enhanced with Post-Quantum Cryptography (PQC) algorithms—CRYSTALS-Kyber for secure key exchange and Dilithium for digital signatures—combined with Zero-Knowledge Proofs (ZKPs) for lightweight device authentication. Neural data packets are logged in a decentralized ledger, ensuring immutability, transparency, and tamper-proof communication. A prototype system was implemented using an EEG-based BCI headset with edge preprocessing and blockchain-secured communication. Experimental results demonstrate a 31% improvement in attack resistance, 22% reduction in latency, and complete removal of central points of failure compared to traditional BCI security models. The novelty of NeuroGuard lies in integrating PQC, blockchain, ZKPs, and edge intelligence into a unified BCI security architecture, paving the way for future quantum-resilient neural communication systems.
This study presents a comprehensive framework that integrates deep learning and blockchain security to address key challenges in cryptocurrency forecasting and privacy preservation. A state-of-the-art ensemble machine learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, is proposed for Bitcoin price prediction. The model achieves 92.1% accuracy on out-of-sample data following rigorous validation, demonstrating strong forecasting performance. To address fundamental security and privacy concerns in blockchain systems, a dynamic privacy framework is proposed, which integrates Zero-Knowledge Proofs (ZKPs) and adaptable consensus methods to improve transaction confidentiality, scalability, and adherence to regulations.
Traditional information system auditing faces severe challenges in data integrity verification and audit transparency. Manual sampling methods are not only inefficient but also vulnerable to data tampering attacks. This paper proposes a blockchain-based auditing framework that integrates SHA-256 hash chains, ECDSA digital signatures, and zero-knowledge proofs to establish a cryptographically secure and tamper-proof auditing environment. The framework employs a three-layer architecture: the data integrity layer uses hash chains to ensure audit records are immutable; the authentication layer uses digital signatures to verify the non-repudiation of evidence; and the privacy layer implements zero-knowledge proofs to protect sensitive data. To verify the framework’s effectiveness, a comprehensive experiment was conducted on a private Ethereum network with five verification nodes, processing 10,000 to 100,000 audit records. The experimental results show a significant performance improvement. Efficiency is improved by $65 \%$, data processing throughput reaches 500 records/second with a response latency of less than 2 seconds, and the average time for hash calculation and digital signature verification is 0.8 milliseconds and 1.2 milliseconds, respectively. Data integrity verification efficiency is improved by $78 \%$ compared with traditional methods, and reliability reaches $99.99 \%$. Comparative experiments show that compared with traditional database-centric auditing systems, the proposed system improves processing throughput by $178 \%$, and reduces manual reconciliation time from 2.5 hours to near real-time. This solution provides a practical, efficient and scalable method for auditing next-generation information systems in enterprise environments.
Decentralized Finance (DeFi) smart contracts manage billions of dollars, making them a prime target for exploits. Price manipulation vulnerabilities, often via flash loans, are a devastating class of attacks causing significant financial losses. Existing detection methods are limited. Reactive approaches analyze attacks only after they occur, while proactive static analysis tools rely on rigid, predefined heuristics, limiting adaptability. Both depend on known attack patterns, failing to identify novel variants or comprehend complex economic logic. We propose PMDetector, a hybrid framework combining static analysis with Large Language Model (LLM)-based reasoning to proactively detect price manipulation vulnerabilities. Our approach uses a formal attack model and a three-stage pipeline. First, static taint analysis identifies potentially vulnerable code paths. Second, a two-stage LLM process filters paths by analyzing defenses and then simulates attacks to evaluate exploitability. Finally, a static analysis checker validates LLM results, retaining only high-risk paths and generating comprehensive vulnerability reports. To evaluate its effectiveness, we built a dataset of 73 real-world vulnerable and 288 benign DeFi protocols. Results show PMDetector achieves 88% precision and 90% recall with Gemini 2.5-flash, significantly outperforming state-of-the-art static analysis and LLM-based approaches. Auditing a vulnerability with PMDetector costs just $0.03 and takes 4.0 seconds with GPT-4.1, offering an efficient and cost-effective alternative to manual audits.
The rapid digitization of commercial, governmental, and legal transactions has created an urgent need for efficient, secure, and transparent dispute resolution mechanisms. Traditional arbitration systems often fall short when handling the complexity and volume of digital evidence, smart contracts, and cross-border interactions. This study proposes a novel AI-powered digital arbitration framework that integrates smart contracts, blockchain-based evidence authentication, and explainable artificial intelligence (AI) to automate and modernize the arbitration process. The framework comprises three core layers: (i) a smart contract-based agreement layer that encodes legal terms and self-executing arbitration clauses; (ii) a blockchain-based evidence management layer that ensures the integrity, authenticity, and traceability of submitted evidence; and (iii) an AI-based arbitration engine that classifies, interprets, and evaluates evidence using transformer and LSTM models, supported by SHAP and LIME for interpretability. A controlled experimental setup was implemented using Ethereum and Hyperledger Fabric testnets, with AI models trained on 1,200 annotated arbitration cases. Results demonstrate a 99.5% reduction in arbitration time, a 92.4% agreement rate between AI and expert rulings, and a 99% accuracy in tampering detection. Furthermore, 87.3% of AI-generated decisions were rated as interpretable and acceptable by legal experts. These findings confirm the system's ability to deliver fast, accurate, and explainable arbitration decisions while complying with legal standards. This research contributes a foundational blueprint for deploying autonomous arbitration systems in digital governance, offering scalable solutions for future applications in smart contracts, e-commerce disputes, and algorithmic legal infrastructure.
To address the limitations of blockchain data storage capacity and uneven dis-tribution, this paper proposes a Chord dual-ring distributed storage method based on virtual nodes. Building upon the original Chord protocol, this approach introduces virtual rings to construct a “storage ring-virtual ring” dual-ring structure. Target virtual nodes are located through routing table lookups, and data is distributed across the storage ring via a name mapping mechanism. Sim-ulation experiments validate the proposed scheme's effectiveness by evalu-ating load balancing and query success rate across varying sharding granulari-ties. Results demonstrate that this approach not only efficiently achieves shard-ed storage for blockchain data but also ensures balanced distribution of block data.
In edge computing, fusing privacy-sensitive heterogeneous sensor data poses challenges in balancing utility, privacy, and efficiency. Existing approaches like zkFL and zkGPT fall short in end-to-end verifiable LLM-driven fusion for non-IID data. We propose a framework embedding ZKPs into adaptive LLM layers for secure multimodal fusion with provable privacy. Key contributions: (1) context-aware attention for LLM fusion; (2) custom zk-SNARK circuits for full verification; (3) dynamic edge optimizations reducing latency by $\mathbf{2 5} \boldsymbol{\%}$. Theoretical analyses provide -DP bounds and convergence guarantees. Experiments on UCI HAR and CIFAR extensions show 91.8% accuracy, MI-AUC of 0.52, and $\mathbf{4 5 ~ m s}$ latency on Jetson Nano, outperforming zkFL by 3.5% in accuracy and 25% in efficiency.
P Arockia Mary, Saranya R, Dharanitha S, Dhanya S · 6 authors
In the contemporary democratic scenario, the secure, transparent, and trustworthy nature of digital elections is of ultimate importance. This objective is achieved via the innovative approach of Hybrid Proof-of-Stake (PoS) and Zero-Knowledge Proof (ZKP) based E-Voting System, which enhances integrity and overcomes challenges faced by the existing models. In contrast to the classical Proof-of-Authority (PoA) model, in which validators are pre-approved, the Hybrid PoS within this system decentralizes the selection of validators by the participation of all network members. It mitigates the risks of centralization and ensures fairness. The voting begins with Voter Registration, wherein Decentralized Identity (DID) establishes that the voter is eligible without revealing any relevant identity information. Through ZKP, voters authenticate their eligibility to vote and, further, acquire a voting token through a smart contract, granting them possible access to the election. Validators are selected dynamically on the basis of stake contributions in that PoS mode to ensure periodic rotation among validators to avoid collusion. In the Vote Casting Phase, ZKP encryption ensures the secrecy of the vote while allowing for its independent verification. Votes are stored immutably on the blockchain, transaction hashes being made available for the independent verification of individual votes. The Vote Validation Phase ensures that PoS-selected validators authenticate the votes and prevent double voting while enforcing the election rules to minimize manipulation risk. Finally, during the Vote Tallying & Result Declaration Phase, the smart contract tallies results and records the information permanently on a blockchain for the purposes of both transparency and protection. This system integrates Hybrid PoS for secure selection of validators and ZKP for privacy-protecting authentication, forming a scalable, fraud-resistant and verifiable e-voting framework.