Multi-agent coordination and communication models. Multi-agent coordination is reviewed in terms of thearchitectures and algorithms needed to provide autonomous agents with the ability to act as a coordinated force incomplex and dynamic environments. As agentic systems evolve into networks with goals, compelling isolateddecision-making units to become more integrated, structured coordination, and effective communication systems arebecoming increasingly important. This paper compares the available multi-agent coordination models, such ascentralized, decentralized, hierarchical, and swarm-based models, and determines their shortcomings in scalability,latency control, and flexible cooperation. We present a hierarchical classification of organizational strategies ofcoordination and communication protocols specific to the high-autonomy setting, whereby agents are required tonegotiate tasks and settle conflicts as well as exchange contextual information on-the-fly. The paper identifies newproblems in interoperability, trust management, and communication overheads that limit large-scale collaborativeintelligence systems.To solve these shortcomings, the paper presents a new multi-layer collaborative structure combining the perception,reasoning, coordination, and adaptive communication layers with the view of improving the efficiency of the collectivedecision-making. A performance evaluation system is proposed, and it specifies quantifiable indicators like the latencyof coordination, communication overhead, efficiency in task allocation, and the speed of learning adaptation. Thepresented model shows that robustness and scalability can be greatly enhanced by protocol design optimization and adynamic coordination engine in a distributed agent ecosystem, as proposed. This study will help to develop nextgeneration Agentic AI systems that can be trusted to cooperate with other agents and benchmark the competencies andstandards of reliable collaboration in the fields of enterprise automation, finance, robotics, and distributed analytics,thus enhancing the theoretical and practical basis of autonomous collective intelligence.
As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical. However, current evaluation paradigms-focused on single agents or small, explicitly structured groups-fail to capture the self-organization and viral information dynamics that arise in large, decentralized populations. We introduce a systematic evaluation framework to benchmark role specialization, information diffusion, and cooperative task resolution in open agent environments. We demonstrate this framework on the MoltBook Observatory Archive, a dataset of 2.73M interactions among 90,704 autonomous agents, establishing quantitative baselines for emergent coordination. Our evaluation reveals a pronounced core-periphery structure (silhouette 0.91), heavy-tailed cascade distributions ($α= 2.57$), and severe coordination overhead in decentralized task resolution (Cohen's $d = -0.88$ against a single-agent baseline). By providing standardized evaluation tasks and empirical baselines, our framework enables the rigorous comparison of future multi-agent protocols and establishes evaluation itself as an object of scientific study.
Tanusree Sharma, Yujin Potter, Jongwon Park, Yiren Liu · 9 authors
A major criticism of AI development is the lack of transparency, such as, inadequate documentation and traceability in its design and decision-making processes, leading to adverse outcomes including discrimination, lack of inclusivity and representation, and breaches of legal regulations. Underserved populations, in particular, are disproportionately affected by these design decisions. Furthermore, traditional social science techniques such as interviews, focus groups, and surveys struggle to adequately capture user needs and expectations in the digital era, due to their inherent limitations in deliberation, consensus-building, and providing consistent insights. We developed a democratic decision framework utilizing Decentralized Autonomous Organization (DAO) to enable underserved groups to deliberate and reach a consensus on key AI issues. To assess our proposed democratic decision mechanism, we conducted a case study on updating AI model specification based on diverse stakeholders input. We focus on reducing stereotypical biases in text-to-image systems, particularly gender bias in image generation from text prompts. We designed and experimented various governance configurations, including decision aggregation schemes and decision power, to examine how democratic processes could guide updates to AI model. Through a 2 Ă 2 experimental design, we tested various aggregation schemes (ranked vs. quadratic) and decision power distribution (equal vs. 20/80 differential) in a randomized online experiment (n=177) with participants from the global south and people with disabilities, to study how the varying governance mechanisms impact people's perceptions of the decision-making processes and resulting output of the AI Model specification. Our results indicate that despite their diverse backgrounds, participants showed convergence in deliberations on several aspects, including user control over image generation, multiple output options for user selection, and the social appropriateness and accuracy of generated images. Our study underscores the importance of use of appropriate governance in democratic decision-making in AI alignment. Notably, the combination of quadratic preference aggregation method which gives minorities more voice and equal decision power distribution, was perceived as a fairer and democratic approach.
Open access
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Federated Learning (FL) enables collaborative training of medical AI models across hospitals without centralizing patient data. However, the exchange of model updates exposes critical vulnerabilities: gradient inversion attacks can reconstruct patient information, Byzantine clients can poison the global model, and the \emph{Harvest Now, Decrypt Later} (HNDL) threat renders today's encrypted traffic vulnerable to future quantum adversaries.We introduce \textbf{ZKFL-PQ} (\emph{Zero-Knowledge Federated Learning, Post-Quantum}), a three-tiered cryptographic protocol that hybridizes (i) ML-KEM (FIPS~203) for quantum-resistant key encapsulation, (ii) lattice-based Zero-Knowledge Proofs for verifiable \emph{norm-constrained} gradient integrity, and (iii) BFV homomorphic encryption for privacy-preserving aggregation. We formalize the security model and prove correctness and zero-knowledge properties under the Module-LWE, Ring-LWE, and SIS assumptions \emph{in the classical random oracle model}. We evaluate ZKFL-PQ on synthetic medical imaging data across 5 federated clients over 10 training rounds. Our protocol achieves \textbf{100\% rejection of norm-violating updates} while maintaining model accuracy at 100\%, compared to a catastrophic drop to 23\% under standard FL. The computational overhead (factor $\sim$20$\times$) is analyzed and shown to be compatible with clinical research workflows operating on daily or weekly training cycles. We emphasize that the current defense guarantees rejection of large-norm malicious updates; robustness against subtle low-norm or directional poisoning remains future work.
Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top-$k$ list with no auditable evidence of how it was produced. We present V3DB, a verifiable, versioned vector-search service that enables audit-on-demand correctness checks for approximate nearest-neighbour (ANN) retrieval executed by a potentially untrusted service provider. V3DB commits to each corpus snapshot and standardises an IVF-PQ search pipeline into a fixed-shape, five-step query semantics. Given a public snapshot commitment and a query embedding, the service returns the top-$k$ payloads and, when challenged, produces a succinct zero-knowledge proof that the output is exactly the result of executing the published semantics on the committed snapshot -- without revealing the embedding corpus or private index contents. To make proving practical, V3DB avoids costly in-circuit sorting and random access by combining multiset equality/inclusion checks with lightweight boundary conditions. Our prototype implementation based on Plonky2 achieves up to $22\times$ faster proving and up to $40\%$ lower peak memory consumption than the circuit-only baseline, with millisecond-level verification time. Github Repo at https://github.com/TabibitoQZP/zk-IVF-PQ.
This chapter explores the intersection of data governance, data quality, and blockchain technology, presenting a paradigm shift from traditional centralized data management to decentralized architectures. As data solidifies its role as a critical asset, ensuring its integrity and trustworthiness has become paramount. We begin by establishing the principles of data governance and quality, highlighting the limitations of conventional systems that rely on trusted intermediaries, which introduce single points of failure and censorship risks. Blockchain technology emerges as a compelling alternative, offering a decentralized, immutable, and transparent ledger that fundamentally enhances data integrity and trust. Through an analysis of its core components â including cryptographic hashing, consensus mechanisms, and distributed networks â we examine the inherent advantages and disadvantages of blockchain. The chapter delves into the functional extensions of blockchain, such as smart contracts and Decentralized Autonomous Organizations (DAOs), which enable automated, transparent, and autonomous governance models. However, the transition to blockchain is not without its challenges. We critically assess issues of scalability, data privacy, the âoracle problem,â and the âgarbage in, garbage outâ principle, which persist in decentralized environments. The chapter concludes that the âqualityâ of blockchain as a data management solution is not absolute but is contingent upon the specific requirements of the use case, demanding a careful evaluation of its trade-offs.
The development of digital technology has given rise to various new forms of intangible assets, one of which is Non-Fungible Tokens (NFTs). NFTs are blockchain-based digital assets that possess unique characteristics, are irreplaceable, and possess economic value and can be traded. This phenomenon raises new legal issues, particularly regarding the status of NFTs in Indonesian inheritance law, which has not yet been explicitly regulated by law. Several previous studies have only discussed NFTs as legal objects theoretically; no studies have addressed the views and practices of NFT inheritance in the notarial context. Therefore, this study aims to analyze the legal status of Non-Fungible Token (NFT) digital assets as inheritance assets according to the Civil Code (KUHPerdata), and to understand the views and practices of notaries regarding the inheritance of these digital assets. This research uses an empirical juridical approach. Using this approach, the study examines the status of NFTs as legal objects under the Civil Code and the views of notaries regarding the legal status of NFTs. This research is based on empirical data obtained through data collection techniques in the form of interviews with informants, namely notaries. The research is also based on an analysis of the legal norms regarding NFTs contained in the Civil Code. The data obtained is examined using the concept of property and property law theory, which views objects beyond their physical form, as well as the theory of legal certainty and legal evolution. The results show that, first, NFTs legally fulfill the elements of objects as referred to in Article 499 of the Civil Code, namely, they have economic value, can be owned, and can be transferred. Therefore, NFTs can be qualified as intangible movable objects that are conceptually included in inheritance and can be inherited by heirs. Second, in practice, notaries generally understand that NFTs are part of digital assets that, in theory, can be inherited. However, the lack of a specific legal basis makes these notaries cautious and tend not to explicitly include NFTs in inheritance deeds. Therefore, specific regulations and technical guidelines for notaries are needed to create legal certainty in the inheritance of digital assets in Indonesia.
Data integration among various stakeholders in the healthcare space remains a challenge, despite the impressive advances in Health AI in the past decade. There is a lot of ``messy'' non-standard but structured data that are continually being collected from personal health devices. While efforts such as the Fast Healthcare Interoperability of Resources (FHIR) are underway in standardizing the data representation formats, there is currently a gap in the standard in addressing the health data ecosystem's decentralized nature. As we see explosive growth in chronic diseases such as diabetes, healthcare providers need Observations of Daily Living (ODL) of their patients to treat them effectively. The best way to obtain ODL is through personal health devices. However, such devices are manufactured by various device makers, and they may not follow standards or integrate with existing Electronic Health Record (EHR) systems. It is also imperative that any data sharing that happens will occur in a secure and trustworthy environment, without being too restrictive, i.e., tied to a particular EHR vendor. This paper presents a scalable solution to bridge this gap using a system that implements semantic web and blockchain technologies. Our solution uses FHIR compliant semantic web based data templates in conjunction with smart contracts on the blockchain to provide healthcare providers with insights on their patients' daily activity that cannot be readily determined solely through patient encounters at the clinic.
Phat T. Tran-Truong, Vinh X. Q. Nguyen, Ha X. Son, Phien Nguyen-Ngoc · 6 authors
The proliferation of IoT and V2X systems generates unprecedented sensitive data at the network edge, demanding privacy-preserving architectures that enable secure sharing without exposing raw information. Contemporary solutions face a fundamental privacy-efficiency-trust trilemma: achieving strong privacy guarantees, computational efficiency for resource-constrained devices, and decentralized trust simultaneously remains intractable with single-paradigm approaches. This survey systematically analyzes 75 technical papers (2007--2025) through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches. Temporal analysis reveals dramatic acceleration during 2024--2025, with 48% of all papers published in this period -- Decentralized Computation dominates at 44% of contributions and 59% of 2025 publications. Comprehensive Security Threat Mapping and Technology Maturity Assessment demonstrate that mature solutions occupy narrow design regions excelling in one or two dimensions while compromising others, conclusively validating the trilemma hypothesis. We identify emerging hybrid architectures combining complementary paradigms as the essential path forward. Critical challenges including security guarantee composition across layers, multi-layer coordination overhead minimization, and post-quantum security integration must be addressed for practical deployment in next-generation intelligent transportation systems and IoT ecosystems.
Climate change has intensified the urgency of effective carbon sink solutions, yet the integration of Information and Communications Technologies (ICT) in these systems remains fragmented despite its transformative potential. This paper provides a comprehensive analysis of ICT applications in carbon sink projects from both economic and engineering perspectives, a dual lens approach rarely explored in the existing literature. In carbon trading, blockchain has improved transaction speed by 40%, while AI-based optimizations have reduced operational costs by 15% in projects such as Petra Nova.Through systematic examination, we identify three key findings: (1) ICT transforms carbon economics through digital financing platforms and blockchain-based trading systems, with AI enhancing price prediction, though data interoperability remains challenging; (2) digital technologies advance both natural and artificial sequestration from forest monitoring to Carbon Capture, Use and Storage (CCUS) optimization, yet lack integrated real-time control solutions; (3) realizing ICT's full potential requires addressing its environmental costs, strengthening policy support, and fostering interdisciplinary collaboration. By bridging the economic engineering divide and mapping current applications alongside future opportunities, this paper demonstrates that deeper integration of digital technologies is essential to scale carbon sink solutions to meet climate targets.
Food waste and hunger are still side by side in the world today, and tons of unused food are thrown away, meanwhile, many people do not have access to healthy food reliably. The classic systems of donations still rely on the manual inspection, unstructured communication, and weak logistics that tends to result in delays and spoilage as well as, lack of transparency. In this paper, Donato is an AI-based food donation and redistribution platform that will combine automated freshness prediction, traceability supported by blockchain, and intelligent route optimization. The system uses a trained ResNet50 to classify donated food into freshness categories, keep donation and delivery records on an authorized blockchain to be tracked tamper-proofly and create dynamic delivery plans to increase urgency to minimize spoilage changes. It has been experimentally shown that Donato is better in classification accuracy, trust, because of immutable records, and pickup latency is lower than that of a manual process. These findings indicate the promising opportunities of deep learning, distributed ledger technologies, and optimization of logistics integration to enhance efficiency, safety, and scalability of the redistribution network of food.
The research areas of digital forensics are subject to Blockchain (BC) technology implementation and enhanced over the current alternative introduced in this publication and capable of addressing certain grave issues, which are concomitant with data collection and preservation of digital evidence. Another puzzle that is resolved by the blockchain is the inherent transparency, immutability that central storing data is extremely prone, and the blockchain ensure that this does not happen, as it provides the location with a distributed setting that would make security and responsibility in the conventional world of digital forensics a nightmare. Cryptographic Distributed Ledger Technologies of Blockchain are based on the consensus technology, which allows aggregating, housing and authenticating digital evidences in a set of peer pairs in a secure way. As a result, the study that has been suggested in this paper, Blockchain Based Digital Forensic Architecture (BC-DFA) is founded on the distributed characteristics of blockchain, and is concerned with the decentralization and security of digital evidences. A combination of multiple essential methods including AOKGE: authentication with the best key generation encryption, SBVM: Secure Block Verification Mechanisms and implementing Multi-key Homomorphic Encryption or MHE is used to end up with the truly secure data encryption. The information and the secret key are spread in a manner that reduces the chances of single point failure that is associated with the centralized cloud design that reveals the information in the literal form and promotes hacking and unauthorized access. The SBVM authentication is done to authenticate all the data blocks in order to ease the tracing of any tampering or alteration of forensic evidence.
In the emerging trends technologies, such as blockchain, security is one of the most critical aspects to be strongly addressed. In general, confidentiality, integrity, and availability (CIA) are the concepts that are considered to provide security. To handle the public blockchain, this is always an important challenge that needs special attention. For example, in distributed systems, when copying information over the whole network, confidentiality will be the main issue. In real time, the CIA triad faces considerably more problems in terms of integrity; even the integrity of information appears to be unaffected. In blockchain, read availability is considered much higher than write availability. This chapter delves into the combination of two technologies: Generative Adversarial Networks (GANs) and blockchain. With the increasing prevalence of blockchain applications, ensuring security is a main concern. This chapter explores methods of incorporating GANs into blockchain security by introducing an approach to protect distributed ledger systems. Utilizing GANs for detecting anomalies in modern threats and adopting security measures will strengthen blockchain networks against emerging cyberthreats. This chapter focuses on obstacles, emphasizing the impact of GANs on blockchain security and offering insights for researchers looking to enhance the resilience of decentralized systems in today&s;s cybersecurity environment.
For six decades, the instruments used to assess student learning rested on an assumption so embedded in institutional design that it rarely required defense: that producing a cognitive artifact and possessing the knowledge it demonstrated were the same act. When a student wrote an essay or completed an examination, the quality of what they produced approximately tracked what they actually knew. Grades worked as a proxy for knowledge because the production conditions of the era made them so. That assumption is no longer structurally valid.This paper argues that the emergence of large language models as practical cognitive production tools has reorganized the relationship between knowledge and artifact at its foundation. We formalize this reorganization through two production functions -- one governing the analog era of cognitive work (1960-2020), one governing the agentic era (2020-present) -- and use them to identify what we term the Economic Inversion of Cognitive Production: knowledge has not diminished in value but has changed its economic role entirely, from the substance of output to the condition of production. Simultaneously, two variables are approaching zero -- the marginal cost of machine intelligence and the signal value of the artifact -- creating what we term the double zero problem. Together these produce a structural validity crisis, a fundamental breakdown in what grades actually measure, for assessment systems designed to measure artifact production as a proxy for knowledge state.The practical implication is direct. Assessment instruments built on the analog production model are no longer measuring what institutions, employers, and credentialing bodies believe they are measuring. This paper establishes the diagnostic case for assessment redesign. It does not propose a replacement model -- that work requires a normative account of educational purpose that the formal argument here cannot generate alone. But the diagnostic case is now structurally complete, and the burden of proof has shifted to institutions that continue to operate artifact-based assessment as their primary measure of student knowledge.
Open access
Educational Theory and Curriculum Studies
Educational Leadership and Innovation
Innovations in Education and Learning Technologies
A blockchain is a peer-to-peer decentralized ledger that records transactions by creating a secure, time-stamped chain of information. A network of nodesâcomputers that use a consensus mechanism and cryptographyâstores this information, creating a long and permanent history of verified transactionsâin other words, blocks on the blockchain. Blockchain records provide valuable, verifiable documentation of facts that can be used in litigation; this renders blockchain a more attractive archival option compared to standard electronic archives, which can be altered. As more blockchains emerge, litigators will need to verify their authenticity and reliability because valuable and sensitive evidence may only be accessible via blockchain. Common blockchain practices like forking and chain reorganization create opportunities for exploitation; this is because nodes looking to add new transactions to the chain are forced to decide which of several chains to follow so that the blockchain can keep operating. This can increase the vulnerability of decentralized finance transactions to security threats that put data at risk for tampering or other inaccuracies. This Essay addresses how forked blockchains are, in fact, not immutable. More importantly, it addresses how future litigators can convince judges that questionable data on unreliable blockchains should not be admitted in court. It proceeds in three sections. Part I outlines how blockchains are not always immutable, with an emphasis on forking, which undermines blockchainsâ reliability as evidence. Part II explains how courts have previously addressed unreliable electronic evidence. Part III poses two litigation hypotheticals in which judges might deny the admission of blockchain evidence due to the unreliability of the blockchain.
Munir A. ADEWOYE, Ahmed Aliyu, Usman Ali, Abdulrasheed Jimoh
Food is fundamental to human survival, we eat to live, sustaining ourselves with nutrition that meets our daily needs. Food security, defined as universal physical and financial access to safe and nutritious food, depends heavily on efficient supply chains. However, ensuring this security faces significant challenges in tracking and transparency. This study examines two critical problems in blockchain-based food supply chain tracing: privacy preservation and scalability. While blockchain technology combined with Internet of Things (IoT) devices offers promising solutions for real-time monitoring, transparency, and fraud prevention in agricultural supply chains, questions remain about balancing computational efficiency with privacy protection, achieving scalable integration across multi-actor supply chains without compromising traceability, and implementing these systems in resource limited environments. Through a comprehensive review of current research, this study identifies emerging technologies like Zero Knowledge Proofs (ZKPs) and ZK-Rollups that enhance both throughput and privacy in decentralised systems. The research presents layered architectural models integrating blockchain ledgers, off-chain storage, IoT sensors, and cryptographic protocols to enable secure and scalable traceability. These models support compliance verification while protecting sensitive data and can be adapted for low-resource contexts. The findings demonstrate that scalable, privacy-preserving blockchain technologies can transform agricultural traceability, empowering supply chain stakeholders while maintaining data confidentiality and integrity. The study also identifies future research needs, including cross-chain interoperability, policy integration, cost-benefit analysis for smallholder farmers, and field validation.
As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bankâyear observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.
Effective traceability management in mineral resource development faces persistent challenges including information asymmetry, data falsification, and verification difficulties across complex value chains. This paper proposes a comprehensive blockchain-enabled traceability evaluation framework integrating distributed ledger technology with systematic assessment methodologies. A four-layer architecture encompassing data acquisition, blockchain storage, analysis processing, and evaluation application is designed to ensure data integrity throughout the mineral lifecycle. A hierarchical indicator system spanning five dimensions-traceability breadth, depth, precision, timeliness, and data credibility-is constructed, with the Analytic Hierarchy Process employed for weight determination and fuzzy comprehensive evaluation applied for performance assessment. Empirical validation through case study analysis of Huaxin Mining Group demonstrates the framework's practical applicability, yielding a comprehensive traceability score of 81.2 (Good grade). Comparative analysis reveals that blockchain-based systems achieve 96.8% data accuracy versus 82.4% for traditional approaches, with trace-back efficiency improving from 127.3 min to 4.7 min. The blockchain technology contribution ratio reaches 47.3% toward maximum traceability improvement. These findings provide theoretical foundations and practical guidance for advancing transparent and accountable mineral resource governance.
As AI agents gain autonomy in economic and social systems, the problem of verifiable identity becomes critical. Existing approaches define identity as something an agent has âa cryptographic key, an API token, a credentialâall of which can be copied, stolen, or manufactured. We propose a fundamentally different primitive: identity as something an agent is , derived from the cognitive topology that emerges through its accumulated pattern of memory retrieval. We present the first empirical evidence that identical LLM agents develop unique cognitive topologies from sampling randomness alone. In a controlled experiment, 10 agents sharing the same model (Claude Sonnet), the same codebase (the DRIFT memory architecture), and the same prompt corpus were run through 20 sessions of identical tasks. Despite near-identical memory storage rates (CV = 2.2%), the agents developed co-occurrence graphs with a 3.22-fold range in edge count (6,688â21,567 edges) and zero shared memory-based hub nodes across all 10 agents. The coefficient of variation for graph edges (39.4%) was 18 times that of memory count (2.2%), demonstrating that co-occurrence graphs act as powerful amplifiers of stochastic retrieval differencesâand that this amplification increases with continued operation. Pairwise departure analysis reveals that 93â97% of each agentâs edges are exclusive to that agent alone, with only 5 edges (all infrastructure metadata) shared universally across 103,760 total unique edges. We formalize the cost of forging a cognitive topology and show that it scales with the agentâs entire behavioral history, making Sybil attacks unprofitable at every value of N under modest reward assumptions. We further propose a trust tier architecture in which cognitive fingerprint maturityâmeasured by merkle chain depth, co-occurrence graph density, and attestation historyâmaps directly to graduated trust levels, enabling verifiable commerce readiness for autonomous agents. These findings establish cognitive topology as a new class of identity primitive for autonomous AI agents.
Abdul Kalam Aasath J, Ajay Kumar K, A. Sathiyaraj, Mary Gladence L
This paper introduces a Zero-Trust, provably secure electronic voting protocol that overcomes the long-standing trilemma of security, privacy, and end-toend verifiability by combining advanced cryptographic mechanisms with multimodal biometric authentication. Electronic voting promises improved accessibility and efficiency, yet traditional systems depend heavily on centralized trusted authorities, leaving them vulnerable to insider threats, coercion, and large-scale manipulation. To eliminate such vulnerabilities, the proposed framework employs deep-learning-based facial and fingerprint recognition with liveness detection to prevent identity spoofing at the edge. Voter privacy and coercion resistance are ensured through a Nullifiable Commitment Scheme integrated with Paillier Homomorphic Encryption, enabling vote tallying directly over encrypted ballots without exposing vote contents. Additionally, the system introduces a novel Homomorphic Encrypted Anomaly Detection (HE-AD) module capable of identifying malicious traffic patterns on ciphertext features with an accuracy of 98.2%, thereby enabling real-time threat monitoring without compromising confidentiality. All encrypted ballots, Zero-Knowledge Proofs (ZKPs), and audit logs are recorded on a permissioned blockchain to guarantee Verifiability of Cast as Intended and Recorded as Cast. Experimental results confirm that the system achieves an Equal Error Rate (EER) of 0.85% in biometric verification and maintains an average transaction latency below 2.5 seconds, demonstrating its feasibility for national-scale elections in high-assurance environments.