Dynamic spectrum sharing (DSS) is essential for 6G networks, yet existing blockchain-based DSS solutions often lack an integrated approach that simultaneously addresses trust, allocation fairness, and system scalability. This paper proposes HierSpectrumChain, a hierarchical blockchain framework that incorporates a global main chain, localized sub-chains, and a smart-contract based Stackelberg auction for credible and automated spectrum allocation. The system model formalizes interactions among spectrum holders, secondary users, and sub-chain validators, enabling transparent bidding and decentralized coordination. A proof-of-concept implementation on an Ethereum Ganache environment evaluates the functional correctness of the auction workflow and measures throughput under varying client loads. While the evaluation is limited to a single-node testbed, the results demonstrate the feasibility of the proposed architecture and establish a basis for future multi-peer experiments on permissioned blockchains. This work provides a coherent design and initial validation for blockchain-enabled DSS in 6G networks.
K.Kalaiselvi, Mohammad Musa Al-Momani, E.Sivajothi, T. Vijetha · 6 authors
Embedded systems are at the heart of critical infrastructure facilities in areas such as energy, transportation, healthcare, defense; where integrity, traceability, and auditability of data generated by the system are of great importance. However, existing data provenance security solutions for embedded settings are highly unsatisfactory because centralized design is vulnerable in embedded environment, they do not scale well and have poor privacy mechanisms. This work presents a novel method of combining immutable data provenance and anonymous blockchain solutions to solve these issues and promote the trustworthiness of embedded systems. The framework takes advantage of privacy-preserving cryptographic methods such as ring signatures, stealth addresses, and zero-knowledge proofs to support tamper-evident decentralized storage of data events without losing source privacy. It's designed to run efficiently under the resource constraints of the embedded platforms, to be low point compatible with low-power devices, without sacrificing the responsiveness of the system or the authenticity of the data. The architecture is designed for real time monitoring and auditability on distributed embedded devices that are installed in critical infrastructure networks. A lightweight consensus algorithm designed for embedded environments allows secure synchronization and validation of data without the need for the heavy computation of a public blockchain. The framework was experimentally validated through prototype implementation and simulation in multiple use-case scenarios, showing its effectiveness against data forgery, unauthorized access and provenance tampering. Performance evaluation demonstrates that the model is scalable, low latency and high throughput under restrained resource environments. This work demonstrated that, by building immutable and anonymous data provenance into embedded systems, in addition to increasing transparency, trustworthiness, and robustness of operation, it is also possible to lay the foundation for a novel class of secure, decentralized infrastructure monitoring tools suitable for adversarial deployments. Results demonstrate a robustness for deployment into actual applications with high-assured data traceability supported with privacy protection.
In today's digital age, cloud storage and computing have become indispensable. Resource-constrained clients such as individuals and small organisations increasingly rely on powerful servers to store, manage and process their data. However, outsourcing data to external servers leads to significant privacy concerns, particularly when dealing with sensitive information such as medical records, financial transactions, or personal data. Fully homomorphic encryption (FHE) is a cryptographic technique that allows computation over encrypted data. In secure outsourcing with FHE, a client sends encrypted data to a server, which can perform requested computations without accessing the original data. The server returns the resulting ciphertexts, which the client can decrypt to obtain the final output. Despite its strong privacy guarantees, the practical adoption of FHE is limited by two main challenges: efficiency, which arises from the substantial performance overhead of FHE; and integrity, which stems from the lack of mechanisms to verify the correctness of the outsourced computation. In this thesis, we contribute to addressing these challenges in three aspects. First, we optimise oblivious algorithms for use in FHE, achieving improvements in key performance metrics and accelerating both bootstrapping and a range of applications. Second, we build efficient privacy-preserving information systems based on FHE. These include (i) two private machine learning protocols, the k-nearest neighbour algorithm and decision tree evaluation, (ii) SQUID, a secure system for storing and analysing genotype-phenotype data, and (iii) a protocol for securely delegating zero-knowledge proof generation. Third, we construct verifiable secure delegation of computation through FHE techniques. We provide the notion of blind proofs to provide integrity guarantees and demonstrate its practicality using blind zkSNARKs, a concrete instantiation of blind proofs.
In an era of rising nationalistic populism and shifting global power dynamics, African healthcare systems remain precariously dependent on Western aid frameworks and, increasingly, China’s profit-driven digital health expansions. This dependency perpetuates structural inequities, leaving nations vulnerable to external agendas while stifling local innovation. This qualitative, exploratory study interrogates the potential of blockchain technology to reconfigure healthcare financing from a paradigm of donor reliance to one of autonomous, equitable resource mobilization. Focusing on Africa, the research critically examines emerging models—such as tokenized health bonds and blockchain-based aid tracking—that could decentralize financial sovereignty, enhance transparency, and foster self-sustaining health ecosystems. The study contrasts Western philanthropic approaches, often entangled with conditionalities and bureaucratic inefficiencies, against China’s strategic, commercialized health infrastructure investments, probing how blockchain might offer a third way—leveraging decentralized finance (DeFi) to reclaim agency. Key questions include: How can blockchain mitigate the politicization of aid in an age of populist retrenchment? Can smart contracts and tokenization democratize health financing while ensuring accountability? Drawing on stakeholder interviews and policy analysis, the presentation argues that blockchain’s disruptive potential lies not merely in technological innovation but in its capacity to recalibrate power dynamics—positioning African nations as architects, rather than beneficiaries, of their health futures. By centering African perspectives, this research challenges deterministic narratives of technological solutionism, instead framing blockchain as a contested but potent tool for decolonizing health financing. The findings aim to provoke debate on the intersection of decentralized technologies, post-colonial autonomy, and the urgent need for equitable health sovereignty in a fragmenting global order.
Decentralized Finance (DeFi) faces a "Capital Inefficiency Trilemma," relying on overcollateralization due to a lack of off-chain financial identity. This paper introduces Zolvency, the Zero-Knowledge Solvency Layer—a cryptographic infrastructure bridging this gap. By utilizing zkTLS for authenticated data extraction from Web2 sources (e.g., tax portals, banking apps) and zkVMs (SP1) for verifiable computation, Zolvency issues privacy-preserving Soulbound Tokens (SBTs) that attest to financial solvency. This enables under-collateralized lending for Real World Assets (RWA) in emerging markets, unlocking liquidity while preserving user privacy and data sovereignty.
The detection of illicit cryptocurrency transactions remains a significant challenge due to the extreme class imbalance and limited generalization capabilities of machine learning models applied to Anti–Money Laundering (AML) data. In the widely used Elliptic dataset, illicit transactions represent less than 2% of all nodes, creating a high-risk setting in which models can achieve deceptively high training accuracy while failing to meaningfully identify malicious behavior. This study examines the behavior of Graph Neural Networks (GNNs) under these constraints and emphasizes the limitations rather than the performance of the approach. Instead of treating the model’s high training accuracy as a success, we demonstrate how imbalance, structural sparsity, and label noise impede reliable learning. We evaluate the model with and without common imbalance-handling strategies including class weighting and focal lossand illustrate that performance remains unstable. Furthermore, we investigate the explainability of the model using GNNExplainer, showing example subgraphs and salient features for known illicit nodes, and discuss their alignment with money-laundering patterns such as fan-out and transaction mixing. Our findings underscore the difficulties of applying GNNs to heavily imbalanced AML datasets and highlight the need for improved modeling strategies, semi-supervised techniques, and more robust explainability methods for real-world financial crime detection.
P. Prakash, Faheema Kattakath Sanil, Jeffrey Tom Shaji, Saravanan Palani · 5 authors
The adoption of privacy-preserving techniques in healthcare is significant, especially while handling sensitive medical information. Traditional machine learning approaches raise significant concern regarding privacy, regulations, and data protection. Federated learning has emerged as an effective machine learning approach that enables a group of local models to collaboratively train the global model by sharing their updates instead of sharing the sensitive medical data. Nevertheless, a significant issue with federated learning is its vulnerability to various attacks, including model corruption and data tampering. The authors propose a methodology for developing a secure and privacy-safeguarded collaborative learning model by integrating zero knowledge proof (ZKP) with federated learning (FL). The proposed RS-ZKP methodology utilizes Pedersen commitments within ZKP to verify feature importance, ensuring that they fall within specified bounds without disclosing the actual values. The methodology is validated on two benchmark datasets using metrics accuracy, precision, recall, and F1 score.
The rapid expansion of digital banking ecosystems has intensified the demand for real-time fraud detection architectures capable of operating at cloud scale.As financial transactions increasingly traverse mobile platforms, API-driven services, embedded finance channels, and cross-border payment networks, fraud patterns have become more dynamic, decentralized, and behaviorally complex.This shift has exposed the limitations of legacy rule-based systems, which lack the adaptability, latency tolerance, and threatintelligence integration required to counter emerging risks.To address these challenges, cloud-native risk orchestration layers have emerged as a foundational component of next-generation fraud detection, delivering high-throughput data ingestion, elastic compute, and intelligent decisioning frameworks suited for modern digital banking environments.At a broader level, cloud-native risk orchestration unifies distributed event streams, machine-learning scoring engines, and policy-management modules within a scalable, microservices-based architecture.This enables fraud systems to process high-velocity transactional, behavioral, and device-identity signals with millisecond latency.As the narrative narrows, the paper explores how real-time fraud detection leverages cloud services such as serverless functions, container orchestration, distributed caching, and streaming analytics to enable adaptive detection pipelines.It further examines how federated intelligence, feature stores, and continuous learning loops enhance model accuracy while maintaining compliance with privacy and data-residency requirements.At its core, the proposed framework emphasizes explainability, risk transparency, and operational resilience incorporating alert-triage routing, anomaly-suppression mechanisms, decision traceability, and integration with case-management workflows.By combining cloud-native design principles with advanced fraud analytics, the paper outlines a comprehensive blueprint for financial institutions seeking to modernize their risk-management stack.This unified approach offers a path toward scalable, real-time, and intelligence-driven fraud prevention that adapts to evolving threats while supporting regulatory compliance and customer trust.
Gukanraj S, Jeeva Rekha R, Dharshan M, Mohan Murthy M · 7 authors
Environmental pollution poses a significant threat to public health and ecosystems, demanding advanced methods for real-time monitoring and source identification. Traditional IoT monitoring systems often fail to capture complex spatiotemporal patterns and raise privacy concerns. This paper introduces a robust, privacy-preserving IoT-based environmental monitoring framework integrating Times Net for temporal feature extraction and Spatio-temporal Graph Neural Networks (STAGE) for spatial relationship modeling. The system incorporates Federated Learning with Differential Privacy, Zero-Knowledge Proofs (ZKP) for authentication, and Post-Quantum Cryptography (CRYSTALS-Cyber) for blockchain-secured model updates. Experimental evaluation using real-world IoT data demonstrates a 93.4% prediction accuracy, a 12% privacy gain, and a 35% reduction in communication cost compared to traditional methods. The architecture is scalable, modular, and designed to support real-time, privacy-sensitive environmental monitoring in smart city applications.
Nov 28, 2025·2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies (SISIMPACT)
The proposed privacy-preserving identity management framework in this research is the combination of blockchain technology, zero-knowledge proofs, and adaptive sharding, which improves security, scalability and regulatory compliance and cybersecurity system. In contrast to current blockchain-based identity mechanisms, the presented mechanism involves encryption of identity attributes and distributes them on dynamic permissioned shards with the ability to selectively disclose identity information without leaking sensitive information. Comparison to Sovrin SSI, uPort and Civic show off impressive results, with a$33-63$percent increase in the transaction rate, 32 percent reduction in the duration of verification and 27 percent less time to generate the proofs. Compliance is achieved by reducing the storage overhead during which the immutability is preserved with chameleon hash-based redaction. There is a huge probability of leakage of privacy and efficiency of the consensus is 97 percent, so identity operations are sound and resistant to tampering. The architecture even makes it possible to have safe cross-domain authentication within heterogeneous environment. These results reassure that the framework will provide secure, efficient, and privacy-compliant identity management and thus will be very useful in enterprise, government, and IoT-based cybersecurity environments.
Efficient extraction and integration of pharmacokinetic (PK) data from scientific literature is critical for informed decision-making in drug development. Prior knowledge of PK parameters, particularly from similar compounds, supports first-in-human dosing, parameter estimation, and compound screening, ultimately helping to reduce attrition in clinical trials. While recent natural language processing (NLP) efforts have focused on extracting PK data from unstructured text, these approaches often overlook more comprehensive PK information and essential contextual metadata, which are usually reported in tables. Despite the prevalence and value of these tables, no previous work has systematically addressed the automated extraction of PK data from them. This thesis presents a novel NLP pipeline for identifying, extracting, and structuring PK data from scientific tables. The work addresses a key gap by targeting tables as a rich and underutilised source of PK information. The thesis is structured around four main components. First, a classification system combining supervised learning and prompt-based approaches is developed to retrieve PK-relevant tables from full-text biomedical articles. Second, heuristic and neural named entity recognition approaches are designed to extract PK parameters and associated metadata from table cells, including dose, species, study population, route of administration, units, and other contextual qualifiers. Third, an entity linking pipeline, including rule-based and zero-shot methods, is developed to normalise extracted data to a standardised PK ontology. Finally, the full pipeline is utilised to construct a large-scale PK database from PubMed Open Access articles. The database is evaluated through systematic sampling and manual quality assessment, and proof-of-concept analyses demonstrate how the extracted data can be used to characterise literature-wide reporting trends and explore comparative pharmacological questions. The results of this thesis demonstrate that automated PK table mining is both feasible and scalable, significantly accelerating the curation of high-quality datasets for pharmacometrics modelling. This work presents new open-source annotated corpora, domain-specific NLP methodologies, and practical tools for structuring PK literature, thereby opening the door to scalable, data-driven approaches in early drug development.
Abstract This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Employing high-frequency data, we analyze cross-cryptocurrency volatility dynamics through two complementary approaches: volatility forecasting and connectedness analysis. Our findings reveal three key insights: (i) TS models, particularly the heterogeneous autoregressive (HAR) model, exhibit superior predictive performance over their ML counterparts, with the long short-term memory (LSTM) model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges; (ii) including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins, especially XRP, whereas forecasts for large-cap coins remain stable, indicating more resilient volatility patterns; and (iii) volatility connectedness analysis reveals substantial spillover effects, particularly pronounced during market turmoil, with large-cap assets (BTC and ETH) acting as primary volatility transmitters and mid-cap assets (XRP and LTC) serving as volatility receivers. These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets, offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
This paper introduces a privacy-preserving framework for blockchain systems using the Smart Contract for Data Expiry (SCDE). SCDE governs data registration, retention, and erasure through on-chain policies and off- chain encrypted storage. It combines AES-256 encryption, a Key Management System (KMS) for cryptographic erasure, and Zero-Knowledge Proofs (ZKPs) for verifiable deletion without revealing data. Decentralized Identifiers (DIDs) enable pseudonymization and user accountability. Comparative results show that traditional and off-chain approaches lack automated, verifiable erasure. SCDE achieves full GDPR compliance with moderate overhead, demonstrating that privacy, transparency, and immutability can coexist in decentralized environments
With the rapid development of quantum computing technology, traditional encryption methods face severe security threats in multi-party privacy intersection protocols in federated learning. In this paper, we propose a new protocol based on post-quantum cryptography. Firstly, lattice-based homomorphic encryption and zero-knowledge proof technology are used to achieve key generation and parameter initialization against quantum attacks. Secondly, ciphertext data encoding is carried out to support homomorphic operations. Next, a zero-knowledge proof is used to verify the correctness of the ciphertext intersection calculation. Finally, the protocol is embedded into the federated learning workflow, adaptively adjusting the parameters. Experimental results show that the protocol achieves the NIST (National Institute of Standards and Technology) security level 3, and the privacy leakage rate is less than 1.2%, the communication and computational costs are controllable, and the protocol does not bring great influence to the accuracy of the federated learning model. The experimental results verify that the protocol can provide a reliable protection for the privacy of federated learning data in the quantum era.
The protection of the privacy, integrity, and auditability of big data has emerged as a significant concern as organizations are growing more dependent on multi-cloud data ecosystems which are decentralized in nature. The problems that traditional data governance models are not able to deal with are a lack of transparency, distrust towards information sharing, inconsistent access control, and their tendency to be tampered with. In order to close these shortcomings, the paper presents a Privacy-Aware Big Data Governance Framework (PBGF) which uses blockchain technology, distributed access control, and fine-grained privacy preservation to safeguard the total data lifecycle. The framework has incorporated smart contracts to automate governance policies, decentralized identity to ensure security in the authentication process, and differential privacy to safeguard sensitive attributes in analytics. A multilayer architecture is created to make sure that there is trusted data ingestion, provenance tracking, compliance, and verifiable data use across the stakeholders. Experimental considered performances indicate better data integrity, traceability, and accountability of access with minimal overhead in analytics performance. The solution is a scalable, tamper-resistant, and privacy-compliant governance model that proposed can be used in large big data systems in the fields of finance, healthcare, and smart cities.
Shanmugam N, C.Jehan, Khaled Alqawasmi, N E Chandra Prasad · 6 authors
Cyber infrastructures, as the substrate of digital ecosystems, are more and more the objects of advanced cyberattacks, security protocol violations, untrustworthy third-party interactions. It is essential to provide a resounding guarantee of reliability and to enforce compliance across these infrastructures, at a time when the scale and complexity of data exchange and operational dependencies are increasing. In this paper, we present a new approach, which uses blockchain technology and smart contracts for cybersecurity compliance enforcement and operation resiliency in cyber infrastructures. The approach employs the decentralized, immutable, and transparent natures of the blockchain in order to create a tamper-proof and auditable setting where the cybersecurity policy can be enforced. Smart contractable compliance are able to automate compliance checking, record security events, and take responsive actions without the trust in a centralized entity. These smart contracts are self-executing action protocols that encode cybersecurity procedures and validation standards that take care of itself when every organization do abide by the security standards. Such mechanism ensures anticipatory enforcement on anomalous behavior and automatic remediation action execution, based on predetermined policy-rules. In addition, a layered protocol structure is being incorporated so that it can enforce compliance throughout different infrastructure components e.g., networks, cloud platforms, IoT systems, and critical information systems. The system design is tightly integrated with features such as identity management, threat intelligence sharing, behavior audit, and distributed access control, all of which are achieved via smart contracts. To build trust in transactions and operations in the environment, consensus algorithms and cryptography are employed. The paper also provides simulations and case studies illustrating the viability of the proposed method in important domains e.g., healthcare, finance and industrial control. It is found that such optimization results in significant enhancement on accuracy for compliance enforcement, reliability of the system, as well as response time to security incidents. The distributed architecture of the framework also reduces the possibility of single point failures and provides robust protection against insider attacks. This work adds to the increasing nexus of security and blockchain technology, providing a secure, autonomous trust and compliance protocol for dynamic cyber battlefields. It provides the groundwork for a future where intelligent cybersecurity contracts are enforcing regulatory mandates, automating the audit process and hardening infrastructure resiliency in a way that requires very little human intervention. The model effectively changes the narrative around compliance, because it is no longer static as a obligation but rather a dynamic, enforceable and self-governing trait woven into the composition of digital infrastructure.
This paper investigates the profound influence of Tang Dynasty legacies on the subsequent Song Dynasty's distinct financial-military transformation. While the Tang era was characterized by an expansive cosmopolitanism, a decentralized military, and a complex interplay of central and regional powers, its eventual decline underscored critical vulnerabilities in state governance and defense. The Song founders, keenly aware of these historical precedents, embarked on a deliberate path of institutional re-engineering. This study argues that the Tang's "cosmopolitan crucible"—its blend of cultural openness, economic dynamism, and ultimately, military decentralization—served as a crucial historical backdrop, compelling the Song to forge a highly centralized, civilian-controlled financial-military complex. We explore how Tang's sophisticated, yet often unstable, fiscal mechanisms and its frontier-focused military organization contrasting with the rise of autonomous regional commanders, directly informed Song's innovations in state monopolies, paper currency, and a robust professional army under strict imperial command. By examining both continuity and rupture, this research illuminates the adaptive strategies employed by the Song to consolidate power, ensure stability, and sustain an unprecedented level of economic and military capability, fundamentally reshaping the trajectory of imperial Chinese statecraft.
Nov 28, 2025·2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies (SISIMPACT)
Deepak Singh Rana, Rahul Rathi, Tomi Ete, Sunil Kumar Shah · 6 authors
For this research, a strong framework is suggested for recognizing fraudulent Ethereum transactions by using both ML and DL approaches. As more people adopt Ethereum for DeFi, NFTs and smart contracts, the integrity of the system is being threatened more often. To deal with these difficulties, the work presents a hybrid RFDNN model which is trained over a labeled dataset of 9,841 Ethereum transactions, including 2,179 that are fraudulent. Class imbalance presents a big issue, so an advanced version of the Synthetic Minority Oversampling Technique (ISMOTE) is used to produce better synthetic data samples that make the model more general. To avoid overfitting and increase results in the real world, the methodology stresses feature engineering, data cleaning and adjusting for the right partitions. The research compares how traditional fraud detection works with adaptive ML models and highlights how adapting to new situations can catch more updated as well as advanced fraud. We need to ensure that any automated fraud system is both understandable and can handle large amounts of data for people to put faith in it. This study reveals that staying flexible and re-training models regularly is important due to the growing number of threats. Experiments were done with Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB) and XGBoost, with the hybrid RFDNN model reaching the best results: 97% accuracy, 96% precision, 96% recall and a 97% F1 score.
This research introduces the Decentralized Finance (DeFi) TrustBoost Framework, which combines blockchain technology and Explainable AI to address challenges faced by lenders underwriting small business loan applications from low-wealth households. The framework is designed with a strong emphasis on fulfilling four crucial requirements of blockchain and AI systems: confidentiality, compliance with data protection laws, resistance to adversarial attacks, and compliance with regulatory audits. It presents a technique for tamper-proof auditing of automated AI decisions and a strategy for on-chain (inside-blockchain) and off-chain data storage to facilitate collaboration within and across financial organizations.
The evolution of the global digital financial system is generating two main forms of digital currencies: a centralized currency system, such as Central Bank Digital Currency (CBDC), and a decentralized cryptocurrency system, like Bitcoin and Ether. This study aims to analyze the conceptual differences between the centralized (CBDC) and decentralized (Bitcoin and Ether) models and each operating mechanism. The study also examines how both models impact the stability of the economy and adherence to Shariah principles. Using the qualitative approach and exploratory design, the study examines materials on CBDC, Bitcoin, and Ether. The study collects data from central bank reports, monetary policy documents, academic articles, and technical papers published by relevant institutions. The content analysis method should identify similarities and differences between the currencies in terms of system architecture, infrastructure, technological efficiency, energy, governance and compatibility with Shariah principles. According to the study, CBDC, Bitcoin and Ether represent three distinct paradigms: Bitcoin's decentralized system, through proof-of-work, produces rather limited functionality to emphasise individual freedom and privacy, while Ether innovates the system via a switch to proof-of-stake and smart contracts, which leads to greater functionality. CBDC, on the other hand, maintains a centralized system to ensure monetary stability, but with a compromise on users' privacy. Hence, while maintaining the value of blockchain transparency and traceability without sacrificing economic stability, the study proposes a hybrid approach in order to improve transaction efficiency. The study suggests implementing a regulatory sandbox involving authorities, economists and Shariah experts as an initial test measure of this innovation to ensure security for users and compliance with the principles of Shariah in the development of a healthier digital financial ecosystem.
We present Areon, a family of latency-friendly, stake-weighted, multi-proposer proof-of-stake consensus protocols. By allowing multiple proposers per slot and organizing blocks into a directed acyclic graph (DAG), Areon achieves robustness under partial synchrony. Blocks reference each other within a sliding window, forming maximal antichains that represent parallel ``votes'' on history. Conflicting subDAGs are resolved by a closest common ancestor (CCA)-local, window-filtered fork choice that compares the weight of each subDAG -- the number of recent short references -- and prefers the heavier one. Combined with a structural invariant we call Tip-Boundedness (TB), this yields a bounded-width frontier and allows honest work to aggregate quickly. We formalize an idealized protocol (Areon-Ideal) that abstracts away network delay and reference bounds, and a practical protocol (Areon-Base) that adds VRF-based eligibility, bounded short and long references, and application-level validity and conflict checks at the block level. On top of DAG analogues of the classical common-prefix, chain-growth, and chain-quality properties, we prove a backbone-style $(k,\varepsilon)$-finality theorem that calibrates confirmation depth as a function of the window length and target tail probability. We focus on consensus at the level of blocks; extending the framework to richer transaction selection, sampling, and redundancy policies is left to future work. Finally, we build a discrete-event simulator and compare Areon-Base against a chain-based baseline (Ouroboros Praos) under matched block-arrival rates. Across a wide range of adversarial stakes and network delays, Areon-Base achieves bounded-latency finality with consistently lower reorganization frequency and depth.