We model the ultimate price paid by users of a decentralized ledger as resulting from a two-stage game where Miners (/Proposers/etc.) first purchase blockspace via a Tullock contest, and then price that space to users. When analyzing our distributed ledger model, we find: - A characterization of all possible pure equilibria (although pure equilibria are not guaranteed to exist). - A natural sufficient condition, implied by Regularity (a la [Mye81]), for existence of a ''market-clearing'' pure equilibrium where Miners choose to sell all space allocated by the Distributed Ledger Protocol, and that this equilibrium is unique. - The market share of the largest miner is the relevant ''measure of decentralization'' to determine whether a market-clearing pure equilibrium exists. - Block rewards do not impact users' prices at equilibrium, when pure equilibria exist. But, higher block rewards can cause pure equilibria to exist. We also discuss aspects of our model and how they relate to blockchains deployed in practice. For example, only ''patient'' users (who are happy for their transactions to enter the blockchain under any miner) would enjoy the conclusions highlighted by our model, whereas ''impatient'' users (who are interested only for their transaction to be included in the very next block) still face monopoly pricing.
Digital certificate forgery remains a real problem in education and employment because traditional verification processes rely on centralized databases, are vulnerable to manipulation, and often take a long time. This study designs and implements a blockchain-based digital certificate verification system that models certificates as Non-Fungible Tokens (NFTs) using the ERC-1155 standard on the Manta Pacific Layer 2 network and incorporates a Soulbound Token (SBT) mechanism to ensure that certificates cannot be transferred. The research adopts a prototyping method through eight stages, starting from architecture design and prototype development to the integration of ERC-1155 smart contracts with IPFS and wallets, as well as testing of minting functions, QR code-based verification, and rejection of asset transfers. The results demonstrate successful on-chain certificate issuance with significantly reduced transaction costs compared to ERC-721-based certificates on Layer 1 networks reported in previous studies, while maintaining a decentralized audit trail. The SBT implementation successfully rejects every attempt to transfer certificates to other wallets, thereby preventing the sale or illicit transfer of credential ownership. These findings indicate that the combination of ERC-1155, SBT, and IPFS on a Layer 2 network has strong potential as an efficient, secure, and practically adoptable digital certificate verification model for educational institutions.
Akkaraju Sailesh Chandra, Lakshmi Iyer, Helen Josephine V.L, Nisha Shankar
This research investigates the integration of machine learning algorithms within blockchain-based Decentralized Autonomous Organizations (DAOs) to enhance operational efficiency, resource allocation, decision-making, and governance. While DAOs provide a transparent and trustless mechanism for digital collaboration, they face challenges related to scalability, bias, data privacy, and coordination. We propose a novel framework that leverages supervises learning models for predictive analytics, reinforcement learning for autonomous decision-making, and unsupervised learning for anomaly detection in DAO voting and resource usage patterns. The study also addresses security and privacy risks by incorporating federated learning and homomorphic encryption. Our proposed model demonstrates improved throughput, decision accuracy, and fairness, as evidenced by performance benchmarks against traditional DAO implementations. The findings suggest that machine learning can significantly optimize DAO architecture and contribute to a more scalable, democratic, and intelligent decentralized ecosystem.
Proof-of-Stake (PoS) has become a widely adopted low-energy consensus paradigm, yet its incentive mechanism remains vulnerable to strategic deviations under complex temporal and network conditions. Existing analyzes rely heavily on theoretical reasoning or manually crafted scenarios, leading to limited coverage and substantial expert overhead. This paper presents PoSitive, an automated framework for systematically uncovering incentive weaknesses in PoS consensus. PoSitive establishes a closed-loop workflow composed of four cooperative modules: a scenario construction module that generates diverse and controllable adversarial configurations, a scenario execution module that faithfully reproduces validator interactions, an outcome evaluation module that quantifies incentive deviations and consensus instability, and a policy optimization module that employs reinforcement learning to iteratively refine attack strategies and explore a broader strategic space. Experimental results demonstrate that PoSitive can effectively identify incentive-layer security risks. Using this framework, we uncover three previously unknown attack strategies, and comparative experiments further confirm the significant role of the policy optimization module in enhancing both attack quality and success rate.
Information and Cyber Security
Infrastructure Resilience and Vulnerability Analysis
Vivekrabinson K, C Yogesh, Indra Kumar M, Hari Suriya K · 6 authors
The rapid progress of generative AI has already seen the rise of highly realistic artificial and deepfake content that has created a problems related to trust on information, privacy, issues related to cyber security loss of general trust. This research introduces TrueSightQ, a unified full stack web application framework through with multimodal detection of AI generated content through use of Quatnum enhancement and Web3 Integration. This system is a hybrid between heuristic and deep learning methods with the added feature of GPU accelerated training combined with quantum advantage classifiers. The trustworthiness and transparency of blockchain technology, as well as IPFS storage and Ethereum Smart contract to improve verification process. The model is further enhanced with modality wise fusion and, decentralized trust based mechanisms for the defense mechanism to adversarial attack. Results show that in general TrueSightQ significantly outperforms standard unimodal detectors overall, with additional gains to verifiability, precision and interpretability demonstrating how the multimodal and decentralized methodology within the model mitigates the issues of AI generated content very efficiently.
Affective artificial intelligence has made substantial advances in recent years; yet two critical issues persist, particularly in sensitive applications. First, these systems frequently operate as 'black boxes', leaving their decision-making processes opaque. Second, audit logs often lack reliability, as the entity operating the system may alter them. In this work, we introduce the concept of Immutable Explainability, an architecture designed to address both challenges simultaneously. Our approach combines an interpretable inference engine - implemented through fuzzy logic to produce a transparent trace of each decision - with a cryptographic anchoring mechanism that records this trace on a blockchain, ensuring that it is tamper-evident and independently verifiable. To validate the approach, we implemented a heuristic pipeline integrating lexical and prosodic analysis within an explicit Mamdani-type multimodal fusion engine. Each inference generates an auditable record that is subsequently anchored on a public blockchain (Sepolia Testnet). We evaluated the system using the Spanish MEACorpus 2023, employing both the original corpus transcriptions and those generated by Whisper. The results show that our fuzzy-fusion approach outperforms baseline methods (linear and unimodal fusion). Beyond these quantitative outcomes, our primary objective is to establish a foundation for affective AI systems that offer transparent explanations, trustworthy audit trails, and greater user control over personal data.
Damilare Peter Oyinloye, Mohd Sameen Chishti, Jingyue Li
Single-bridge blockchain solutions enable cross-chain communication. However, they are associated with centralization and single-point-of-failure risks. This paper proposes Proof of Success and Reward Distribution (PSCRD), a novel multi-bridge response coordination and incentive distribution protocol designed to address the challenges. PSCRD introduces a fair reward distribution system that equitably distributes the transfer fee among participating bridges, incentivizing honest behavior and sustained commitment. The purpose is to encourage bridge participation for higher decentralization and lower single-point-of-failure risks. The mathematical analysis and simulation results validate the effectiveness of PSCRD using two key metrics: the Gini index, which demonstrates a progressive improvement in the fairness of the reward distribution as new bridge groups joined the network; and the Nakamoto coefficient, which shows a significant improvement in decentralization over time. These findings highlight that PSCRD provides a more resilient and secure cross-chain bridge system without substantially increasing user costs.
N Mangala, Murtaza Rangwala, S Aishwarya, B Eswara Reddy · 8 authors
Healthcare has become exceptionally sophisticated, as wearables and connected medical devices revolutionize remote patient monitoring, emergency response, medication management, diagnosis, and predictive and prescriptive analytics. Internet of Things and Cloud computing integrated systems (IoT-Cloud) facilitate sensing, automation, and processing for these healthcare applications. While real-time response is crucial for alleviating patient emergencies, protecting patient privacy is paramount in data-driven healthcare. In this paper, we propose a multi-layer IoT, Edge, and Cloud architecture to enhance emergency healthcare response times by distributing tasks based on response criticality and data permanence requirements. We ensure patient privacy through a Differential Privacy framework applied across several machine learning models: K-means, Logistic Regression, Random Forest, and Naive Bayes. We establish a comprehensive threat model identifying three adversary classes and evaluate Laplace, Gaussian, and hybrid noise mechanisms across varying privacy budgets, with supervised algorithms achieving up to 83.6% accuracy. The proposed hybrid Laplace-Gaussian noise mechanism with adaptive budget allocation provides a balanced approach, offering moderate tails and better privacy-utility trade-offs for both low and high-dimension datasets. At the practical threshold of $\varepsilon$=5.0, supervised algorithms achieve 80-81% accuracy while reducing attribute inference attacks by up to 18% and data reconstruction correlation by 70%. We further enhance security through Blockchain integration, which ensures trusted communication through time-stamping, traceability, and immutability for analytics applications. Edge computing demonstrates 8$\times$ latency reduction for emergency scenarios, validating the hierarchical architecture for time-critical operations.
The rising demand for collaborative machine learning and data analytics calls for secure and decentralized data sharing frameworks that balance privacy, trust, and incentives. Existing approaches, including federated learning (FL) and blockchain-based data markets, fall short: FL often depends on trusted aggregators and lacks Byzantine robustness, while blockchain frameworks struggle with computation-intensive training and incentive integration. We present \prot, a decentralized data marketplace that unifies federated learning, blockchain arbitration, and economic incentives into a single framework for privacy-preserving data sharing. \prot\ enables data buyers to submit bid-based requests via blockchain smart contracts, which manage auctions, escrow, and dispute resolution. Computationally intensive training is delegated to \cone\ (\uline{Co}mpute \uline{N}etwork for \uline{E}xecution), an off-chain distributed execution layer. To safeguard against adversarial behavior, \prot\ integrates a modified YODA protocol with exponentially growing execution sets for resilient consensus, and introduces Corrected OSMD to mitigate malicious or low-quality contributions from sellers. All protocols are incentive-compatible, and our game-theoretic analysis establishes honesty as the dominant strategy. We implement \prot\ on Ethereum and evaluate it over benchmark datasets -- MNIST, Fashion-MNIST, and CIFAR-10 -- under varying adversarial settings. \prot\ achieves up to 99\% accuracy on MNIST and 90\% on Fashion-MNIST, with less than 3\% degradation up to 30\% Byzantine nodes, and 56\% accuracy on CIFAR-10 despite its complexity. Our results show that \prot\ ensures privacy, maintains robustness under adversarial conditions, and scales efficiently with the number of participants, making it a practical foundation for real-world decentralized data sharing.
Many recommender systems in long-form video streaming reply on batch-trained models and batch-updated features, where user features are updated daily and served statically throughout the day. While efficient, this approach fails to incorporate a user's most recent actions, often resulting in stale recommendations. In this work, we present a lightweight, model-agnostic approach for intra-day personalization that selectively injects recent watch history at inference time without requiring model retraining. Our approach selectively overrides stale user features at inference time using the recent watch history, allowing the system to adapt instantly to evolving preferences. By reducing the personalization feedback loop from daily to intra-day, we observed a statistically significant 0.47% increase in key user engagement metrics which ranked among the most substantial engagement gains observed in recent experimentation cycles. To our knowledge, this is the first published evidence that intra-day personalization can drive meaningful impact in long-form video streaming service, providing a compelling alternative to full real-time architectures where model retraining is required.
Ravisankar M, Raghunandhan V, Senthil Pandi S, T. Kalai Selvi · 6 authors
In the rapidly evolving healthcare sector, the accurate forecasting and secure management of health insurance claims are crucial for both insurance providers and their clients. This study introduces an integrated framework that merges machine learning algorithms with blockchain infrastructure to enhance fraud prevention and optimize the prediction and processing of health insurance claims. The primary objective is to accurately predict claim amounts using advanced regression modeling and to streamline the entire claims administration process via an open, transparent, and decentralized blockchain network. For the predictive component, the XGBoost Gradient Regression algorithm was implemented. To complement this forecasting capability , A blockchain-based system is proposed for managing health insurance claims. This system enables secure collaboration and data exchange among key stakeholders, including hospitals, insurance firms, laboratories, and third-party administrators. The blockchain layer ensures data integrity, transparency, and security, substantially reducing opportunities for fraud, processing delays, and administrative overhead. By uniting the predictive power of machine learning with the trust and efficiency of blockchain, this project offers a robust, intelligent, and secure solution for modern health insurance management.
This paper proposes a distributed computing framework that integrates federated learning (FL) and blockchain-enabled smart contracts for automatic flood response management. FL is deployed to process time series sensor data locally for separate sensor devices by training a machine learning (ML) model and then aggregating the trained model parameters obtained from each sensor device to yield final predictions in terms of rainfall levels. The predicted precipitation levels are input into a predefined smart contract to automatically trigger mitigation strategies to be used by frontline safety and maintenance personnel. The results obtained using the proposed framework demonstrate both improved prediction accuracy and data privacy preservation. The validation effort shows that smart contracts can execute context-aware actions, thus enabling fast decision-making for flood response. The developed framework holds the potential to revolutionize decentralized data management, enhance efficient data processing, and ensure data privacy, transparent and secure data communication, and resilience against centralized failures, thereby enabling a more intelligent infrastructure management system to mitigate flood impacts.
Vinoth Punniyamoorthy, Ashok Gadi Parthi, Mayilsamy Palanigounder, Ravi Kiran Kodali · 6 authors
Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture that integrates federated learning, differential privacy, zero-knowledge compliance proofs, and adaptive governance powered by reinforcement learning. The framework supports secure model training and inference without centralizing sensitive data, while enabling cryptographically verifiable policy enforcement across institutions and cloud platforms. A full prototype deployed across hybrid Kubernetes clusters demonstrates reduced membership-inference risk, consistent enforcement of formal privacy budgets, and stable model performance under differential privacy. Experimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The proposed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.
The article presents an analysis of the robustness of an authentication scheme based on zero watermarking. The study examines a two-factor authentication scheme that uses "knowledge of something" (a password) and "possession of something" (a digital RGB image) as its factors. The zero watermarking algorithm chosen is based on DWT and K-means transformations, with additional use of the Swish function. The analysis is conducted by considering the theoretical complexity of the algorithm assuming the adversary knows its parameters, such as the password, the hash of the password, the image, the reference watermark, the transformation result, and other parameters. Previous studies have shown a high theoretical robustness of the scheme, which relies on the complexity of the password and the dimensionality of the image. For large image sizes (512×512 pixels and above), a relatively high level of cryptographic resistance is achieved. However, this robustness is not formally proven, and the actual strength may be significantly lower due to the specifics of the images and transformations, which can introduce additional vulnerabilities. The algorithm is subject to a relatively high rate of collision, associated with digital image transformations and matrix multiplications, which weakens its resistance. Authentication schemes and zero watermarking algorithms require further research, formal proof of cryptographic properties, and methods for integration into access control systems, as they can provide a high level of authentication robustness in systems with high noise levels. Additionally, the convenience and low cost of such schemes give them an advantage over other authentication methods. The study provides recommendations for improving the potential characteristics of the algorithm.
Open access
Advanced Steganography and Watermarking Techniques
Yaxian Dong, Zijun Zhan, Daniel Mawunyo Doe, Zhu Han · 5 authors
In the project-oriented construction industry, recruiting qualified workers who can finish the required tasks in a limited time is important. However, the high turnover rate in the construction workforce poses a challenge in verifying applicant information, leading to potential issues like information falsification and inaccurate assessments due to information asymmetry. Additionally, the industry’s male-dominated nature may foster stereotype-based biases, particularly concerning sensitive attributes (e.g., gender). Such situations contribute to unfair competition among applicants. The construction industry is also experiencing new technologies like BIM, AI, and Blockchain. Their integration shows potential for automation, fairness, information security, and trustworthiness in recruitment. To build a diverse and competent workforce, we propose a decentralized digital resume-based job applicant screening and appraisal framework via BIM, AI, and Blockchain. First, we develop a blockchain job applicant data model that distinguishes between personal privacy data and work-related data for record and storage. A permissioned Blockchain is then designed to facilitate partial transparency for potential employers while ensuring the confidentiality of applicants’ sensitive information. Specifically, for personal privacy data, sensitive attributes (gender, race, etc.) are safeguarded via encryption, and data (address, etc.) about company preferences (the desired distance range from the company, etc.) is also secured while allowing for employer verification via Zero-Knowledge Proofs and smart contracts for information protection. Utilizing time-stamped authentication, applicants’ work history (reference network-based and performance-based information) remains immutable and is securely accessible by potential employers. Based on the validated applicant data and diverse company requirements, the digital resume is generated and customized for each position through smart contracts. For validation, a prototype system is developed with the data from LinkedIn. The results show its feasibility for trusted, fair, secure, and effective construction recruitment.
Decentralized finance has introduced new forms of market making through automated market makers, where users provide liquidity to decentralized exchanges such as Uniswap. In Uniswap v3, liquidity providers (LPs) can concentrate their liquidity within custom price ranges, improving capital efficiency but introducing exposure to impermanent loss and nonlinear portfolio risk. Managing this exposure dynamically poses a challenge as the LP position’s token composition changes continuously with market prices. This thesis investigates whether a Deep Reinforcement Learning (DRL) agent can effectively hedge a Uniswap v3 LP position using cryptocurrency futures. A simulated Uniswap environment is developed to model concentrated liquidity providing positions and the hedge positions tied to it. The hedging problem is framed as a sequential decision process, where the agent seeks to minimize downside portfolio variance while maintaining upside exposure. The Proximal Policy Optimization algorithm is applied to learn an adaptive hedging policy, which is evaluated against baseline strategies such as fixed-frequency rebalancing. The results show that the DRL-based hedges can outperform the baseline strategies, achieving higher average portfolio returns, with similar average drawdowns. However, the learned policies varied between agents, and although they reduced downside variance in many cases, they were also more likely to experience larger maximum drawdowns. These findings indicate that reinforcement learning offers a promising but complex approach for managing the nonlinear risks of concentrated liquidity provision in decentralized exchanges.
Tokenisation is the process by which real-world assets or services are converted into digital tokens on a distributed ledger. The digital token becomes an on-chain representation of the real-world asset and can be managed by participants who have access to the distributed ledger as part of a blockchain network.
The rapid proliferation of Internet of Things (IoT) devices across various industries, including healthcare, smart cities, and industrial automation, has introduced significant security, authenticity, and traceability challenges within increasingly complex supply chains. Although existing approaches have utilised blockchain-based digital identity solutions to address some of these concerns, persistent issues of counterfeit products and inadequate lifecycle transparency highlight the need for more robust, hardware-anchored identification mechanisms. Our work presents a novel architecture that integrates Physically Unclonable Functions (PUFs) and blockchain-based Soulbound Tokens (SBTs) to establish secure and verifiable digital identities directly tied to the physical hardware of IoT devices. By employing cryptographic tools such as fuzzy extractors, Merkle trees, and zero-knowledge proofs, the proposed architecture ensures accurate lifecycle tracking through key operational stages, including manufacturing, procurement, provisioning, maintenance, and eventual disposal or recycling. Performance evaluations conducted on the Ethereum Sepolia testnet demonstrate reasonable computational overhead in terms of gas usage and transaction confirmation times. The findings reveal that this approach aligns with NIST Special Publication 800-161 guidelines, as well as emerging regulatory standards, notably the European Union’s Digital Product Passport initiative, and has significant implications for enhancing transparency, sustainability, and security across global IoT supply chains.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Nur Shahidah Mohammad, Md. Mafizur Rahman, Md. Abdur Razzaque
Federated Learning (FL) enables multiple entities to collaboratively train models without sharing sensitive data, but it faces critical privacy, security, and efficiency challenges in healthcare intrusion detection systems. These issues are intensified by adversarial attacks, non-IID data, and the need for real-time performance. Existing FL methods struggle with gradient inversion, model poisoning, Sybil attacks, and high computational overhead, limiting their effectiveness in secure and scalable healthcare applications. This work proposes the PrivacyPreserving Personalized Federated Learning Intrusion Detection in Healthcare applications (P3FL-HIDS), integrating Byzantinerobust aggregation, gradient masking, and Zero-Knowledge Proof based authentication. Key features include strong adversarial resilience, protection of privacy against gradient inversion, personalized model adaptation for heterogeneous data, and secure participant authentication. Additional contributions include a dual-network training approach, adaptive clustering for personalization, and optimized secure communication for real-time healthcare scenarios. Experimental results on a Brain Tumor magnetic resonance imaging (MRI) dataset show that P3FLHIDS outperforms state of the art works in terms of accuracy, resilience, and resistance.
Blockchain is often referred to as a Distributed Ledger Technology or DLT. One could argue that there are two-components to ledger-keeping: the ledger itself (the data) and the process of “keeping” it or amending the data.
Abu Talha Haque Miah, Miss Al Ive, Roby Mohajon, Zahroul Haque Miah
Dependability of electricity continues to present challenges in remote and displacement-affected areas of Bangladesh (Cox’s Bazaar) in a humanitarian context. This paper presents a field-based model for decentralized solar energy deployment using a third-party financing mechanism, implemented in Camp 7 by Ulterior Engineering under CODEC in partnership with UNHCR. A 3.5 kW hybrid off-grid solar photovoltaic (PV) system was installed to power essential community services and operationalized within fourteen days. The pilot combined technical and institutional innovations, including a profit-sharing investment model, accelerated implementation, shared-risk contractual agreements, and realtime performance monitoring. The system architecture included Jinko 580 W modules, a 4 kW inverter, and LiFePO4battery storage at the Tulips facility, while the Sunflower facility used 250 W Fortune Solar modules. The results revealed that more than 75% of the daily consumption was covered by solar energy, largely diminishing diesel fuel consumption. Financial viability was ensured with an internal rate of return at 15%, a payback period at 5.9 years, and emissions avoidance of 77 t CO2over 20 years. The system had yielded a competitive levelized cost of energy at USD $0.034 / \mathrm{kWh}$, complementing its economic sustainability with a high IRR value of 15%. Indeed, this model sets a replicable framework for private investment in humanitarian energy access both within off-grid and resourceconstrained environments.
This study examines the interrelation among gold, oil, and cryptocurrency markets and their implications for economic growth in the context of geopolitical turmoil. Employing panel data from 2000 to 2023 of exporter, importer, and mixed economies, we employ Nonlinear Autoregressive Distributed Lag (NARDL) and Panel Vector Autoregression (PVAR) to ascertain asymmetric as well as dynamic relations. Evidence shows that oil and gold price shocks exert significant effects on growth with geopolitical risk increasing volatility, while cryptocurrencies are heterogeneously resilient in panels. The results provide fresh evidence of cross-asset linkages, risk transmission mechanisms, and provide policy implications for policymakers and investors under volatile geopolitical environments.