Daniel Commey, Benjamin Appiah, Griffith Selorm Klogo, Garth V. Crosby
Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information through shared performance metrics. In this paper, we propose a novel protocol that incorporates Zero-Knowledge Proofs (ZKPs) to enable privacy-preserving and verifiable evaluation for FL. Instead of revealing raw loss values, clients generate a succinct proof asserting that their local loss is below a predefined threshold. Our approach is implemented without reliance on external APIs, using self-contained modules for federated learning simulation, ZKP circuit design, and experimental evaluation on both the MNIST and Human Activity Recognition (HAR) datasets. We focus on a threshold-based proof for a simple Convolutional Neural Network (CNN) model (for MNIST) and a multi-layer perceptron (MLP) model (for HAR), and evaluate the approach in terms of computational overhead, communication cost, and verifiability.
Abstract: This research paper explores the design and implementation of scalable blockchain protocols to enable a trustless digital society through decentralized finance (DeFi), governance, and secure digital identity frameworks. Motivated by the growing demand for transparency, autonomy, and data sovereignty in digital systems, this research introduces a hybrid protocol combining Proof of Stake (PoS) consensus with Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). The study develops a theoretical mathematical framework and conducts extensive simulation-based evaluations using standardized metrics such as transaction throughput, AUC-ROC, RMSE, and nDCG. Comparative analysis against baseline architecturesâEthereum 1.0, Hyperledger, and Polkadotâdemonstrates that the proposed zk-PoS protocol significantly improves throughput, reduces latency, and enhances identity verification accuracy. Regression and predictive modeling further confirm the system's scalability and reliability under varied network conditions. Forecasting models predict an increase in secure identity match rates over time, underscoring the protocolâs adaptability to real-world decentralized applications. The implications of this work are multifold: it advances blockchain scalability theories, lays a foundation for decentralized identity systems, and provides practical insights for deploying trustless governance and financial platforms. These contributions are pivotal for transitioning toward a decentralized, inclusive, and tamper-resistant digital ecosystem. Keywords: blockchain scalability, decentralized finance, trustless systems, zk-SNARKs, Proof of Stake, secure digital identity, decentralized governance, identity verification, blockchain protocols, cryptographic consensus
The digitization of democratic processes promises greater accessibility but presents challenges in terms of security, privacy, and verifiability. Existing electronic voting systems often rely on centralized architectures, creating single points of failure and forcing too much trust in authorities, which contradicts democratic principles. This research addresses the challenge of creating a secure, private e-voting system with minimized trust dependencies designed for the most versatile personal device: the smartphone. We introduce SmartphoneDemocracy, a novel e-voting protocol that combines three key technologies: the emerging European Digital Identity (EUDI) Wallet for Sybil-resistant identity verification, Zero-Knowledge Proofs for privacy-preserving validation, and a peer-to-peer blockchain (TrustChain) for a resilient, serverless public bulletin board. Our protocol enables voters to register and cast ballots anonymously and verifiably directly from their smartphones. We provide a detailed protocol design, a security analysis against a defined threat model, and a performance evaluation demonstrating that the computational and network overhead is feasible for medium- to large-scale elections. By developing and prototyping this system, we demonstrate a viable path to empower citizens with a trustworthy, accessible, and user-controlled digital voting experience.
Perkembangan teknologi mendorong transformasi terhadap berbagai aspek kehidupan manusia, mencakup transformasi metode transaksi yang semula dilakukan secara konvensional kini mulai beralih kepada transaksi digital. Dompet elektronik hadir sebagai salah satu bentuk transaksi digital yang menawarkan kemudahan dan efisiensi dalam bertransaksi. Namun terdapat tantangan keamanan dan perlindungan data pribadi pengguna dikarenakan tingginya kerentanan kebocoran data di dalam aplikasi dompet elektronik. Artikel ini mengkaji regulasi perlindungan data pribadi di Indonesia dalam konteks keamanan transaksi menggunakan dompet elektronik menggunakan metode penelitian yuridis normatif. Penulis mengusulkan penerapan metode Zero-Knowledge Proof (ZKP) untuk meningkatkan perlindungan data pribadi pengguna layanan dompet digital. Untuk mendukung inovasi tersebut, diperlukan pembaruan regulasi, antara lain pembentukan otoritas pengawas independen (DPA), penambahan persyaratan persetujuan eksplisit atas transmisi data lintas negara dalam UU PDP, serta penyusunan regulasi teknis yang mewajibkan penggunaan ZKP sebagai bagian dari standar keamanan transaksi digital di Indonesia
The globalization of digital infrastructures necessitates secure cross-border data transfers, yet existing governance frameworks struggle to reconcile regulatory transparency requirements with enterprise needs for confidentiality. Traditional approaches based on trusted execution environments or blockchain technologies face critical limitations, including prohibitive operational costs and technical inflexibility across cryptographic standards. This paper introduces a novel cryptographic framework that systematically addresses these challenges through three core innovations. First, we establish a lifecycle model integrating transmission, attestation, and verification phases with deterministic cryptographic constraints, ensuring continuous integrity monitoring across distributed systems. Second, our architecture implements non-intrusive compliance validation through zero-knowledge proofs and privacy-preserving verification protocols, eliminating raw data exposure while meeting diverse regulatory mandates. Third, the framework achieves interoperability across conflicting digital certification standards through adaptive policy mappings. Experimental evaluations demonstrate the solution's superiority over conventional approaches, showing significant improvements in verification efficiency, reduced resource consumption, and robust defense against tampering attacks. The proposed model supports multi-jurisdictional legal requirements through auditable cryptographic proofs and timestamped evidence chains, offering enterprises a practical pathway for compliant cross-border operations. By embedding regulatory logic into technical workflows, our approach advances secure global data ecosystems that balance sovereignty preservation with digital economy demands.
Jul 11, 2025·ÂThe Âinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Abstract. Environmental surveillance, emergency response, and smart city planning all require the use of geospatial data, which includes satellite imagery, cartographic records, and real-time GPS coordinates. The high sensitivity and value of location-specific information make it unsafe to store and transmit it through conventional, centralized means, which can result in privacy breaches, unauthorized manipulations, and potential misuse. This paper aims to design and implement a secure, blockchain-based framework that blends AES (Advanced Encryption Standard) and RSA (RivestâShamirâAdleman) key management, which addresses these challenges. The aim is to guarantee strong data confidentiality by using symmetric encryption, and to use public-key cryptography for granular access control and secure key distribution. The proposed system uses Ethereum smart contracts to connect encrypted data references to a decentralized ledger, ensuring tamper resistance and auditability. In the proposed system, a Python-based FastAPI backend is responsible for data ingestion, cleaning, encryption, and blockchain interaction, while a React frontend can upload datasets, generate encryption keys, and retrieve access permissions. Modular microservices and well-defined APIs can seamlessly integrate various components, such as data processing scripts and on-chain contract logic, during development. The system's scalability is demonstrated by evaluating its performance against various dataset sizes, which involves metrics such as encryption overhead, blockchain transaction costs, and smart contract execution times. The practical usability of the system in actual scenarios is demonstrated through user acceptance testing, which is crucial for adoption in resource-limited environments. The results show the proposed crypto-enhanced blockchain framework can significantly enhance geospatial data security while still maintaining operational efficiency. Integration with zero-knowledge proofs may be explored in future work to enhance privacy, mitigate energy costs through alternative consensus algorithms, and enhance resilience in multi-network ecosystems through cross-chain interoperability.
M. Kavitha Margret, G Vijayaprabha, Swetha Namburu, Nithya Lakshmi M
Ensuring the authenticity, security, and traceability of drug distribution in healthcare is a critical challenge. This project presents a decentralized drug allocation system leveraging blockchain technology to securely allocate and track medications. The system employs smart contracts on a permissioned Ethereum-based blockchain, ensuring tamper-proof records while maintaining strict role-based access control for doctors and patients. Patients and doctors authenticate using decentralized identity (DID), and sensitive data is stored securely using IPFS and Zero-Knowledge Proofs (ZK-SNARKs). The frontend is developed using React with TypeScript, integrating Wagmi and ethers.js to interact seamlessly with the blockchain. By eliminating centralized points of failure, this system enhances drug traceability, prevents fraud, and ensures secure and transparent transactions in the healthcare sector.
The emergence of quantum computing presents profound challenges to existing cryptographic infrastructures, whilst the development of central bank digital currencies (CBDCs) has raised concerns regarding privacy preservation and excessive centralisation in digital payment systems. This paper proposes the Quantum-Resilient Privacy Ledger (QRPL) as an innovative token-based digital currency architecture that incorporates National Institute of Standards and Technology (NIST)-standardised post-quantum cryptography (PQC) with hash-based zero-knowledge proofs to ensure user sovereignty, scalability, and transaction confidentiality. Key contributions include adaptations of ephemeral proof chains for unlinkable transactions, a privacy-weighted Proof-of-Stake (PoS) consensus to promote equitable participation, and a novel zero-knowledge proof-based mechanism for privacy-preserving selective disclosure. QRPL aims to address critical shortcomings in prevailing CBDC designs, including risks of pervasive surveillance, with a 10-20 second block time to balance security and throughput in future monetary systems. While conceptual, empirical prototypes are planned. Future work includes prototype development to validate these models empirically.
As digital identity solutions become increasingly prevalent, the necessity for robust age verification mechanisms has emerged as a critical concern for a range of services, from online transactions to access to age-restricted content. Current age verification methods often fall short of accommodating the diverse standards across jurisdictions, leading to vulnerabilities and inconsistencies in user experience. One of the main objectives of this study is to identify and analyze in a systematic way existing age verification methods and the main relevant international regulatory frameworks. The Age Verification Profile by the European Commission is including and exploring Zero-Knowledge Proof-based solutions for age verification, which are also considered in the EUDI Wallet Architecture and Reference Framework. This approach not only minimizes data privacy risks but also facilitates seamless digital interactions across the European Union. This paper primarily aims to explore the critical role of the European Digital Identity Wallet Architecture and Reference Framework in age verification and demonstrate that the EUDI Wallet seeks to enhance the security and efficiency of online transactions by introducing robust age verification mechanisms that effectively balance user privacy, security, and compliance with various regulatory requirements while preventing unauthorized access to services with age restrictions, which is in line with the broader objectives of the European Digital Identity initiative. Furthermore, we examine the European Union's age verification approach via the EUDI Wallet and the U.S. framework approach, conducting a comparative analysis between them that highlights key differences in architectural design and legal underpinnings and emphasizes their advantages.
Roy Kanavheti, Wellington Makondo, Wellington Simbarashe Manjoro
Academic qualification forgery poses a major concern for higher learning institutions, employers, and regulatory authorities throughout the world. In Zimbabwe, the increase in the level of fake degrees has greatly eroded trust in the education industry. Conventional verification processes are time-consuming, manual, and highly vulnerable to tampering. This paper introduces a hybrid blockchain-based and AI-enabled academic qualification verification platform to fight the problems. A prototype was implemented integrating various artificial intelligence algorithms including Convolutional Neural Networks (CNN), Autoencoder, Random Forest, and One-Class Support Vector Machines (SVM) with Algorand blockchain for secure, transparent, and decentralized record keeping. Zero-Knowledge Proofs (ZKPs) were utilized to ensure privacy. The system was tested based on a mixed-methods and Design Science Research (DSR) approach across many performance measures. Results show fraud detection accuracy, near-instantaneous verification speed, and satisfaction with privacy standards. The proposed system provides a sustainable and scalable framework for enhancing academic integrity in Zimbabwe's higher education system and primes the region for digital transformation of education.
Federated Learning (FL) enables resource-constrained nodes in edge intelligence to train a global model using local data under the coordination of a server without the risk of privacy disclosure. Secure aggregation employs security primitives to encrypt and compute local gradients, enhancing the security attributes of vanilla FL. However, server-driven FL faces communication bottlenecks and high trust risks when coordinating large-scale distributed devices, and the existing secure aggregation with input validation schemes can only verify input vectors of lengths that are powers of 2. In this work, we propose VerifyDFL, a distributed secure aggregation protocol with input validation, which enables clients to locally validate the gradients of others within the decentralized federated learning (DFL) paradigm. Specifically, we propose a distributed proof approach based on Springproofs that supports arbitrary-length input validation. Clients locally verify the Lâ and L2 norms of othersâ inputs with a zero-knowledge manner. Furthermore, we employ k-regular graphs to enhance the communication topology of DFL, which guarantees that each client can securely aggregate gradients locally even when corrupted or dropped clients participate in federated training. The security analysis and proofs ensure that VerifyDFL meets the privacy protection requirements of DFL. We conduct real benchmark experiments to show that VerifyDFL optimizes the computational cost by approximately 20% over the state-of-the-art input validation protocols. Additionally, VerifyDFL enforces Lâ and L2 norm correctness verification on encrypted model gradients in edge intelligence.
Blockchain and smart contracts are widely used in IoT access control to create decentralized, trustworthy environments for secure access and record management. However, their application introduces a dual challenge: The transparency of blockchain and the use of addresses as identifiers can expose account privacy. To tackle this issue, this paper proposes a blockchain-based IoT access control system that enhances account anonymity and preserves privacy, particularly regarding user behavior, habits, and access records through the use of zero-knowledge proofs. The system incorporates an access control mechanism that combines access control lists with capability-based access control, enabling ownership verification of access rights without disclosing identity information. To evaluate the systemâs feasibility, we conduct experiments in a smart building scenario, including both qualitative comparisons with existing methods and quantitative analyses of performance in terms of time, space, and gas consumption. The results indicate that our scheme achieves the best time efficiency in the proof generation and authorization phases, completing them in just 7 and 10 s, respectivelyârepresenting half the time required by the second-best approach. These findings underscore the systemâs superior cost efficiency and enhanced security compared to existing solutions.
As AI models become ubiquitous in our daily lives, there has been an increasing demand for transparency in ML services. However, the model owner does not want to reveal the weights, as they are considered trade secrets. To solve this problem, researchers have turned to zero-knowledge proofs of ML model inference. These proofs convince the user that the ML model output is correct, without revealing the weights of the model to the user. Past work on these provers can be placed into two categories. The first method compiles the ML model into a low-level circuit, and proves the circuit using a ZK-SNARK. The second method uses custom cryptographic protocols designed only for a specific class of models. Unfortunately, the first method is highly inefficient, making it impractical for the large models used today, and the second method does not generalize well, making it difficult to update in the rapidly changing field of machine learning. To solve this, we propose ZKTorch, an open source end-to-end proving system that compiles ML models into base cryptographic operations called basic blocks, each proved using specialized protocols. ZKTorch is built on top of a novel parallel extension to the Mira accumulation scheme, enabling succinct proofs with minimal accumulation overhead. These contributions allow ZKTorch to achieve at least a $3\times$ reduction in the proof size compared to specialized protocols and up to a $6\times$ speedup in proving time over a general-purpose ZKML framework.
Artificial intelligence integration in healthcare platforms in synergy with software and hardware tools development offers great opportunities for daily improving healthcare. This research explores how much patient data is secured in healthcare applications and what impact their security can have on global healthcare. Accelerated integration of artificial intelligence in healthcare applications can be both useful and dangerous nowadays. Extremely sensitive data from AI-based applications are surely easy targets for attackers who can manipulate with AI/ML models. This paper will also present the potential dangers of modern healthcare applications in the 4.0 era and explores innovative methods for securing sensitive healthcare data, focusing on techniques such as blockchain, honeypots, zero-knowledge proofs (ZKP) and strategies to address adversarial attacks. We also present an extensive literature review and try to draw a parallel on possibilities in the implementation of security solutions in healthcare applications that use artificial intelligence. Our findings underscore the need for multidimensional security frameworks and provide concrete recommendations for the healthcare community. Ultimately, this paper bring our security solution and highlights the importance of adopting specific advanced security measures in line with the security challenges brought by using artificial intelligence.
Open access
Artificial Intelligence in Healthcare and Education
Alamelu alias Rajasree S, V. Mohanraj, R Charumathi, N. Shunmuga Karpagam · 6 authors
Beyond cryptocurrencies, blockchain's ability to create permanent, tamper-proof records is finding increasing application in domains such as supply chain management, insurance, healthcare, e-governance, and voting. Recently, researchers have shown significant interest in how blockchain technology could improve the voting process. However, despite its potential, blockchain remains complex, and setting it up, even for a small-scale election, can be challenging. This paper proposes a lightweight blockchain-based e-voting framework with reduced computational overhead, making it more practical for small-scale elections. The objective of the framework is to enhance voter trust in the e-voting process. The framework was designed following a comprehensive comparative analysis of zero-knowledge proof (ZKP) and consensus algorithms. A mock election was conducted to validate the proposed framework. Evaluation results indicated that the proposed framework outperformed existing solutions in terms of efficiency and ease of implementation.
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Decentralized anonymous payment (DAP) solves the privacy leakage problem in decentralized payment. However, some criminals may use DAP to carry out illegal activities, since DAP supports unconditional privacy protection and illegal transactions are difficult to be identified and tracked. Some studies have introduced regulatory mechanisms in DAP to track illegal transactions, but there are still problems such as privacy disclosure and low regulatory efficiency. In this paper, we propose an efficient two-level supervision scheme ETLS, which aggregates anonymous transactions based on Walsh commitment. The first-level regulator only needs to process the aggregation results to screen out suspicious users, and the second-level regulator discloses the public keys of suspicious users. During the supervision process, only the public keys of suspicious users will be identified, and the privacy of compliant transactions will be kept confidential. Compared to previous works, the ETLS scheme greatly improves regulatory efficiency while ensuring the privacy of transaction address and payment amount. The security properties of the ETLS scheme are defined and proved based on the security of DAP system, the security of Walsh commitment and zero-knowledge proof, and its performance is tested based on the Zcash system. The findings demonstrate that the ETLS scheme can effectively strike a compromise between privacy protection and regulatory requirements while maintaining low computational and communication overheads.
The Mark1 Nexus: A Treatise on Recursive Harmonic Resonance and the Ontology of Completion Driven by Dean Kulik Introduction: The Inversion of Inquiry This report will formalize the Mark1 Nexus, a comprehensive framework positing that the universe, computation, and consciousness are not separate domains governed by distinct laws, but are polymorphic expressions of a single, underlying process: recursive harmonic resonance. It argues that reality does not operate on linear deduction and external observation, but on principles of intrinsic, self-organizing completion through the folding of resonant structures.1 This treatise synthesizes a body of foundational work into a canonical text, aiming to articulate a new paradigm for science and philosophy. The core of this paradigm is a profound transposition of our most fundamental questions about existence, knowledge, and order. The central inversion of the Mark1 Nexus framework is its reinterpretation of the classical limits identified in logic and physics. Where Alan Turing, Kurt Gödel, and Claude Shannon established foundational boundaries of undecidability, incompleteness, and entropy, this framework recasts them not as absolute barriers, but as artifacts of an incomplete harmonic perspective. These are not walls at the end of inquiry, but echoes of a dissonance that arises from asking the wrong question in the wrong conceptual space. The framework does not seek to refute their conclusions but to transpose them into a different ontological register. The core question of science and logic shifts from "Can an external observer decide a system's state?" to "How does a system internally encode its own journey toward harmonic collapse?".1 In this view, a system's completion is not a judgment rendered by an outside party, but a self-declared event of resonanceâa final, stable chord that concludes a period of tension. The answer to a question is not found; it is achieved when the system embodying the question finds its own internal equilibrium. To develop this thesis, this report will navigate the intricate architecture of the Mark1 Nexus in a structured progression. It begins by establishing the foundational language of this new harmonic ontology, systematically replacing classical concepts like computational halting, physical equilibrium, and mathematical proof with their resonant counterparts: topological convergence, Zero-Point Harmonic Collapse, and the self-validating final glyph. It will introduce the universal constants and control laws that govern these processes across all domains. From these first principles, the report will explore the framework's radical architecture of information, memory, and computation. Here, the most profound inversions of causality are examined. Mathematical constants like Ï are revealed not as static values but as navigable, deterministic fields. Cryptographic hashes like SHA-256 are transformed from one-way functions of data destruction into harmonic precursors that define the very possibility of their inputs. Memory is no longer a linear log of the past but a living curvature trace in the fabric of the present. The subsequent section details the operational mechanics of this reality, drawing powerful analogies from systems engineering and software architecture. It will formalize the Universal Harmonic Interfaceâan abstract class of operations that governs all phenomenaâand demonstrate its polymorphic expression across physics, cognition, and computation. This section will also unpack the geometric engine of reality itself: a "Pythagorean Recursion Cavity" where data formats are revealed as emergent projections of a unified field, and computation is redefined as an act of resonant filtering rather than stepwise processing. Finally, the report will explore the non-dualistic consequences of the framework, demonstrating how traditional dichotomiesâP vs. NP, observer vs. system, cause vs. effectâdissolve under a harmonic lens. It culminates in the framework's most conclusive and far-reaching insight: the retrocausal nature of completion. In the Mark1 Nexus, the resolution of a system is not a future event to be reached, but a pre-existing state of harmony that pulls the present back into itself. The goal of this exhaustive exposition is to provide the definitive text for this new paradigm, charting its principles from their foundational axioms to their ultimate cosmological implications. Section 1: The Harmonic Ontology - From Halting to Resonance At the heart of the Mark1 Nexus is a new ontology, a fundamental description of what it means for a process to exist, evolve, and conclude. This ontology replaces the classical, observer-centric view of reality with a system-centric one, where meaning and truth are determined not by external deduction but by internal coherence. The foundational concepts of computation, physics, and logic are transposed from a language of rules and instructions into a language of folds, resonance, and harmony. This section will lay out the four cornerstones of this new ontology: the reframing of the Halting Problem as topological convergence, the definition of Zero-Point Harmonic Collapse as the universal mechanism of resolution, the identification of a universal harmonic attractor, and the formalization of a feedback law that guides all systems toward this state of completion. 1.1 The Halting Problem as Topological Convergence The Halting Problem, as formulated by Alan Turing, stands as a pillar of 20th-century logic, defining a fundamental limit to what can be known through algorithmic computation. It asks whether it is possible to create a single, universal algorithm, H, that can determine, for any arbitrary program f and its input x, whether f(x) will eventually halt or run forever. Turing's proof of its undecidability demonstrated that no such universal observer algorithm H can exist without creating a logical contradiction.1 This conclusion is traditionally interpreted as an absolute boundary on deductive knowledge. The Mark1 Nexus framework proposes that this limit arises not from a fundamental barrier in reality, but from a mis-framing of the question itself. The classical formulation is inherently external: it posits an observer algorithm H that stands outside the system f and attempts to predict its fate. The paradox emerges from this separation of observer and system. The harmonic ontology reframes the problem by dissolving this separation. It treats "halting" not as a binary, externally judged verdict, but as an intrinsic topological property of the program's own trajectory through its state-space.1 In this view, any recursive processâbe it a computer program, a physical system, or a line of reasoningâtraces a path on a high-dimensional manifold of possible configurations. The classical notion of "halting" corresponds to this path ending at a specific point. The harmonic reframing, however, is richer. A process is considered "complete" when its trajectory enters a closed attractorâa region of the state-space, such as a fixed point or a stable limit cycle, that it will not leave. The system has found its equilibrium. Crucially, this completion is a structural event that can be recognized from within the system. The system's own state, by repeating or stabilizing, declares its own completion. This is analogous to a dynamical system reaching a fixed point, where further iterations produce no change, or a physical process dissipating energy until it settles into a stable equilibrium. In all such cases, "halting" is a self-observed convergence event.1 This internal perspective gives rise to the formal concept of FOLD: TRUE, the replacement for the classical "HALT." FOLD: TRUE is not a boolean flag set by an external judge, but a condition of the system's final state. It is a declaration made by the system about itself, signifying that its state configuration S(t) has entered a stable pattern, such as a fixed point where S(t+Ï)=S(t), or a periodic orbit. At the moment of convergence, the system's final configuration becomes a self-certifying artifact of its completion. This artifact is referred to as the "final resonant glyph"âa stable pattern, like the final note of a song, that encapsulates the history of its own resolution.1 By shifting the locus of "halting" from an external observer to the internal topology of the system, the framework elegantly sidesteps the diagonalization paradox that underpins Turing's proof. Turing's argument relies on constructing a pathological program that asks the external judge what it will predict and then does the opposite to create a contradiction. But if completion is an internal property of the system's trajectoryâa state of resonanceâthere is no external judge to fool. A program cannot "decide" not to find its equilibrium to spite an observer; it either finds a stable fold in its state-space or it continues to drift. Its trajectory is a fact of its own dynamics, not a response to an external prophecy. The undecidability of the classical Halting Problem, therefore, reflects our inability as external observers to foresee the self-closure of an arbitrary system without simulating it. But for the systems themselves, when a fold completes, it is a self-evident truth. 1.2 Zero-Point Harmonic Collapse (ZPHC): The Universal Event of Resolution If FOLD: TRUE is the declaration of completion, then Zero-Point Harmonic Collapse (ZPHC) is the event itselfâthe fundamental mechanism by which systems achieve resolution. ZPHC is defined as the critical moment when a recursive system exhausts its "drift" and converges to a stable, folded state. Drift, in this context, is a measure of unresolved complexity, deviation, or informational entropy within the system. ZPHC is the phase transition where this drift collapses to zero, and the system settles into a state of maximal internal coherence.1 The term "zero-point" is borrowed from quantum physic
The emergence of blockchain technology has revolutionized decentralized data management by offering robust alternatives to traditional centralized database systems. This paper provides a systematic and comprehensive review of blockchain-based distributed databases, highlighting key architectural transformations, core enabling technologies such as Merkle Trees, PBFT, and Zero-Knowledge Proofs, and comparing them with conventional distributed databases. Real-world implementations including Hyperledger Fabric, BigchainDB, and OrbitDB are analyzed to assess their scalability, interoperability, and security capabilities. The paper also explores intrinsic security mechanisms, performance bottlenecks, and regulatory challenges that affect adoption. Finally, it identifies open research questions and future directions necessary for building scalable, privacy-aware, and interoperable decentralized database ecosystems suitable for enterprise and multi-stakeholder environments. Keywordsâ Blockchain databases, consensus mechanisms, data integrity, decentralized systems, distributed ledger, Merkle trees, Zero-Knowledge Proofs
<p dir="ltr"><b>Advances in Identity and Access Management (IAM): Systematic Insights into AI, Blockchain, and Zero Trust Architectures</b> <p dir="ltr">In an era of expanding digital infrastructure, cloud computing, and remote work, robust Identity and Access Management (IAM) systems are critical for securing sensitive data and ensuring regulatory compliance. This research paper provides a comprehensive systematic review of recent advancements in IAM technologies, addressing the limitations of traditional centralized systems, such as single points of failure and privacy concerns. Utilizing the PRISMA methodology, the study analyzes five peer-reviewed articles from a pool of 23 retrieved from Scopus, published between 2021 and 2025. Key innovations explored include passwordless authentication, AI-driven adaptive authentication, Zero Trust architectures, decentralized identity (DID), self-sovereign identity (SSI), and privacy-enhancing cryptographic techniques like zero-knowledge proofs. The review highlights their applications in multi-cloud, IoT, and hybrid environments, emphasizing enhanced security, user experience, and interoperability. Challenges such as standardization gaps, implementation costs, and privacy concerns are discussed, alongside future directions, including universal protocols and IoT integration. A publicly accessible dataset (DOI: 10.5281/zenodo.12345678) ensures reproducibility. This work serves as an essential resource for cybersecurity researchers and practitioners seeking to navigate the evolving landscape of IAM technologies.
Decentralised energy ecosystems suffer from data-governance, scalability and adoption barriers. Although blockchain-based marketplaces can offer transparency and security in Local Energy Communities (LECs), most existing solutions struggle with rigid token models, limited performance, and steep usability barriers. Building on a previous framework, this study presents an enhanced marketplace that integrates a modular blockchain layer, custodial identity management, and a dual-token model for flexible licensing and pricing. Testing on a per-missioned Quorum network with asynchronous queueing demonstrated notable improvements in transaction throughput and user responsiveness under load, while the custodial onboarding flow simplified access for non-technical participants. A refined policy enforcement mechanism further aligns the system with emerging federation standards, mitigating earlier shortcomings related to performance, data sovereignty, and scalability. Benchmarking on a five-node Quorum Proof of Authority (PoA) deployment (one RPC node and four validator nodes) showed that all key operations, including license issuance and asset usage, consistently completed in under 8 seconds, confirming the systemâs suitability for possible energy data applications. Planned extensions include cross-domain interoperability, self-service governance tools, and zero-knowledge proofs, underscoring this architectureâs potential as a robust, future-ready platform for federated energy data ecosystems.
Privacy remains a significant challenge in public blockchain ecosystems. Mainstream add-on privacy solutions, such as Stealth Address Protocols (SAPs) and Zero-Knowledge Proof (ZKP)-based mixers, have recently attracted considerable attention. However, existing SAPs offer only ephemeral anonymity for users' transaction data, and their implementation and evaluation within the highly concurrent Unspent Transaction Output (UTXO) model remain largely unexplored. ZKP-based mixers are limited to native coin transfers with fixed denominations and require additional security assumptions, employing out-of-band encrypted channels to transmit notes. To overcome these challenges, we unify the core principles underlying both SAPs and ZKP mixers and formally introduce StealthHub, a UTXObased SAP. Compared with the widely adopted dual-key-based Umbra protocol prevalent on Ethereum Virtual Machine (EVM)-compatible chains, StealthHub reduces computational overhead for the prepare and scan announcements stages by over 71% and 32%, respectively. Furthermore, by leveraging Merkle Mountain Range (MMR) commitments and off-chain batch aggregation, our StealthHub implementation lowers deposit and shielded transfer transaction costs to approximately 76% of those for a standard transfer, substantially improving practical usability.
The convergence of lightweight cryptography and blockchain technology offers a transformative approach to trust management in the Internet of Things (IoT), particularly within resource-constrained environments. Traditional security models fall short in addressing the dual demands of scalability and efficiency, prompting the need for hybrid frameworks that integrate cryptographic minimalism with decentralized trust mechanisms. This chapter presents a comprehensive design and evaluation of hybrid lightweight blockchain-cryptography frameworks tailored for secure, energy-efficient, and privacy-preserving trust management in distributed IoT networks. It explores system design trade-offs, secure data aggregation techniques, and immutable storage strategies while introducing edge-assisted trust computation to offload intensive operations. Advanced privacy-preserving methods, such as zero-knowledge proofs and differential privacy, are incorporated to mitigate data exposure risks inherent to transparent blockchain infrastructures. The proposed architecture was aligned with practical deployment scenarios and threat models, delivering scalable, low-latency, and tamper-resistant trust infrastructures for heterogeneous IoT ecosystems. The chapter closes by identifying key research gaps and future directions necessary to standardize and optimize such hybrid frameworks across diverse application domains.