Real estate in smart cities and the metaverse is being reshaped by NFTs, tokenization, blockchain, AI valuation models, digital twins, and cybersecurity. Tokenization enables fractional ownership, access, and liquidity, while NFTs provide immutable rights that reduce fraud and enhance transparency. AI valuation uses machine learning, predictive analytics, and computer vision to improve pricing and integrate with blockchain for auditability. Digital twins link physical and virtual assets, supporting predictive maintenance, energy efficiency, and immersive walkthroughs. Yet adoption faces risks from smart contract exploits, market manipulation, and quantum computing, requiring quantum-resistant cryptography and privacy tools like zero-knowledge proofs. Case studies highlight Dubai's NFT registry, U.S. pilots, Europe's blockchain registries, and Asia's metaverse platforms. Economically, the market is projected to grow from USD 2.33 billion in 2025 to USD 67.40 billion by 2034, underscoring the need for harmonized laws, ethical AI, and sustainable frameworks.
Abstract - Donation fraud and lack of transparency are major challenges in traditional charity systems, where donors often have limited visibility into how their contributions are utilized. Centralized platforms are prone to data manipulation, unauthorized fund usage, and security breaches, reducing donor confidence. This study explores blockchain-based approaches for securing and accurately managing donation transactions. We review various systems that implement smart contracts, decentralized ledgers, and cryptographic techniques to ensure transparency, traceability, and accuracy in fund distribution. The analysis compares architectural designs, data validation mechanisms, accuracy levels, and security models across existing frameworks. Finally, we highlight current limitations and propose future enhancements to improve scalability, privacy, and real-world implementation of blockchain-based donation management systems. Keywords: Blockchain, Smart Contracts, Donation Security, Transparency, Decentralized Ledger, Cryptography, Ethereum, Zero-Knowledge Proofs, Data Accuracy, Trust Management.
The growth of distributed energy resources and local energy markets heightens the need for price formation that is transparent, privacy preserving, and compatible with network constraints. Blockchain provides a trust-minimized substrate for auditable clearing and settlement through consensus, tamperevident ledgers, and smart contracts. This survey organizes blockchain-enabled pricing into three families, namely auction-based, game-theoretic, and optimization-based, and links them to enabling techniques such as metering oracles, secure multiparty computation, zero-knowledge proofs, and verifiable optimality certificates. Applications span wholesale electricity, carbon and green certificates, distributed energy trading, ancillary services, and electric vehicles. Evidence indicates gains in auditability, privacy, network awareness, and automated settlement, alongside challenges in scalability, data protection, grid integration, and regulation. The survey distills design patterns and research directions toward verifiable, interoperable, and governable pricing modules that complement system-operator markets.
Hang Liu, Ming Yang, Aotian Cai, Chenhao Wang · 5 authors
In recent years, with the increasing prevalence of online group chat applications, malicious information has been more easily disseminated on the internet. Asymmetric group message franking (AGMF) allows users to report received malicious messages to moderators, achieving content moderation in large-scale online end-to-end messaging systems. However, the state-of-the-art construction is built upon traditional public key cryptosystems, resulting in the complex certificate management problem. This paper systematically explores identity-based AGMF (IB-AGMF) to resolve this issue. Specifically, we first introduce a novel primitive called hash proof system-based anonymous identity-based key encapsulation mechanism supporting sigma protocol (HPS-AIB-KEMΣ) and present a practical construction based on DBDH assumption. After formalizing the concept and security notions of IB-AGMF, we propose the generic construction of IB-AGMF based on HPS-AIB-KEMΣand non-interactive zero knowledge proof system. Finally, we conduct comprehensive performance evaluations and comparisons to demonstrate the feasibility of IB-AGMF in group communication scenarios.
Xin Liu, Anyang Qi, Lanying Liang, Dan Luo · 10 authors
In computer vision, the intersection determination of polygonal areas is utilized to segment different regions in an image and assist in detecting the boundaries of the regions. Moreover, the secure computation of the intersection area of polygons can solve the private calculation of geometric problems in machine learning. A security protocol under the semi-honest model was designed for the problem of secure computation of the intersection area of two polygons. This protocol adopts a new coding method and the Paillier homomorphic encryption algorithm. Aiming at the malicious behaviors that malicious participants may carry out in the semi-honest protocol, a secure computation protocol for the intersection area of polygons under the malicious model was designed by using methods such as hash function, cut-and-choose and zero-knowledge proof. The security of this protocol was proved, and its computational complexity and communication complexity were analyzed. Compared with the existing schemes, it is more efficient.
Nan Geng, Can Zhou, Jiafeng Feng, Xin Zhang · 7 authors
The advancing integration of Cyber-Physical-Social Systems (CPSS) within the modern power industry has highlighted the need for enhanced data integrity and multi-entity coordination. In this context, the pursuit of secure and trustworthy lifecycle management for power materials, regarded as a foundational component in ensuring system stability and operational efficiency, has attracted increasing attention. However, existing systems often face limitations such as information opacity, insufficient data accuracy, and the absence of a secure trust mechanism, hindering intelligent development and long-term sustainability. Blockchain technology, distinguished by its distributed ledger, transparency, immutability, and smart contract capabilities, offers a promising solution by enhancing data security and ensuring information reliability. This study introduces a blockchain-based framework for the secure and trustworthy lifecycle management of power materials within CPSS environments, which ensures lifecycle traceability, real-time monitoring, and trustworthy information exchange. By integrating key application scenarios, such as refined equipment management and paperless execution of contracts, the proposed approach addresses crucial operational needs. A multidimensional analysis with conventional systems reveals its advantages in improving management efficiency, optimizing resource allocation, enhancing data security, and reducing operational costs. The proposed framework thus provides both theoretical foundations and practical pathways for leveraging blockchain in power material lifecycle management, enabling digital transformation, managerial innovation, and collaborative industry development.
This paper proposes DAPUR, a decentralized anonymous payment protocol that simultaneously achieves strong transaction privacy and regulatory compliance. Addressing the fundamental tension between confidentiality and oversight in cryptocurrency systems, we develop a novel cryptographic framework combining zero-knowledge proof with access-controlled encryption. The protocol enables end-to-end transaction privacy while permitting authorized regulators to audit transaction details through selective disclosure mechanisms. The system’s security is formally proven through a model establishing ledger indistinguishability, transaction non-malleability, and balance preservation. Experimental results demonstrate practical viability with sub-second verification times. DAPUR represents a significant advance in privacy-preserving payment systems, showing that regulatory compatibility can be achieved without compromising decentralization principles.
Mohammed Al Ghafari, Badar Al Alawi, Idris Aal Jumaa, Salah Al Awaidy
Background/Objectives: Oman Vision 2040, the national blueprint for socio-economic transformation, aims to elevate the Sultanate to developed nation status, with the “Health” priority committed to building a “Leading Healthcare System with International Standards” via a Health in All Policies (HiAP) approach. This paper critically reviews Oman’s strategic health directions and implementation frameworks under Vision 2040, assessing their alignment with global Sustainable Development Goals (SDGs) and serving as a case model for health system transformation. Methods: This study employs a critical narrative synthesis based on a comprehensive literature search that included academic, official government reports, and international organization sources. The analysis is guided by the World Health Organization’s (WHO) Health Systems Framework, providing a structured interpretation of progress across its six building blocks. Results: Key interventions implemented include integrated governance (e.g., Committee for Managing and Regulating Healthcare), diversified health financing (e.g., public private partnership (PPPs), Health Endowment Foundation), and strategic digital transformation (e.g., Al-Shifa system, AI diagnostics). Performance metrics show progress, with a rise in the Legatum Prosperity Index ranking and an increase in the Community Satisfaction Rate. However, critical challenges persist, including resistance to change during governance restructuring, cybersecurity risks from digital adoption, and system fragmentation that complicates a unified Non-Communicable Disease (NCD) response. Conclusions: Oman’s integrated approach, emphasizing decentralization, quality improvement, and investment in preventive health and human capital, positions it for sustained progress. The transformation offers generalizable insights. Successfully realizing Vision 2040 demands rigorous, evidence-informed policymaking to effectively address equity implications and optimize resource allocation.
Cryptocurrencies are increasingly the subject of fake news, increasing risks for market stability and investor decisions. To address this issue, we propose a multimodal framework to detect fake cryptocurrency news using text, image, and sentiment features with BERT, Swin Transformer, and RoBERTa, respectively. We use multi-head attention to combine these features to ensure the complementarity of features from different modalities. The fused representations are passed into a fully connected layer for final classification. Experimental results show that this framework achieves better accuracy and reliability than unimodal and multimodal models for detecting cryptocurrency misinformation.
Sai Srinivas Vellela, Lakshma Reddy Vuyyuru, Sudhir Kumar Jidugu, M. Purnachandra Rao · 6 authors
The concept of blockchain technology has transformed the digital ecosystem to allow decentralized, transparent and immutable transactions in various sectors. Its security is closely dependent on classical cryptography like ECDSA and RSA to perform digital signatures and SHA-256 to achieve consensus, which are becoming more susceptible to quantum computing. The cryptographic principles underpinning blockchain could be compromised with the emergence of the quantum algorithms of Shor and Grover, endangering the integrity of transactions, authentication and consensus protocols. This paper discusses the implications that quantum computing could have on blockchain security, analyses vulnerabilities of current cryptographic primitives and assesses post-quantum cryptographic (PQC) protocols, including lattice-based protocols, hash-based protocols, and code-based protocols. To guarantee backwards compatibility, as well as a gradual upgrade process to quantum-resistant protocols, a hybrid migration approach that involves transactions with two signatures is suggested. Experimental analysis shows that PQC integration attains reasonable performance trade-offs, that preserve verification costs, block size growth, and throughput effects within feasible limits, and that zero-loss resilience is achieved during reorganization tests. The conclusion is that blockchains with PQC support can reach post-quantum levels of security without major operation interruption, and they provide a viable roadmap to moving towards distributed ledger systems that are future-ready.
The real estate sector stands at an inflection point where technological convergence fundamentally reshapes how properties are transacted, recorded, and verified. This chapter explores the integration of advanced cybersecurity protocols, artificial intelligence-powered analytics, and hybrid blockchain architectures to create immutable, transparent, and secure property transaction ecosystems. By merging predictive AI capabilities with distributed ledger technology, property records, ownership verification, and transactional security achieve unprecedented levels of trust and operational resilience. This convergence simultaneously addresses critical challenges in fraud prevention, regulatory compliance, and stakeholder confidence, while streamlining property transfers and risk assessment mechanisms across global real estate markets.
Energy saving is need of hour and effective energy management for Smart grids is no exception. Efficient management of Smart grids is complex task and thus needs state-of-the-art technologies for efficient management and ensuring data privacy. For secured and efficient management the emerging technologies like Federated Learning and Blockchains can be deployed. The integration of Federated learning and Blockchain offers a promising solution for these advanced and decentralized energy management systems. Federated Learning is distributed machine learning approach where the model is trained over multiple decentralized devices. data centralization. Blockchain technology offers, immutable,secured distributed ledger system that complement the Federated Learning framework. The integration of Federated learning and Blockchain facilitates secure tamper proof data analytics, which can transform energy management systems. The present research paper proposes a model for Smart grids which is based on integration of FL and BC technologies. The study discusses System Architecture for FL-BC framework. Study also proposes the possible simulation for real-world smart grid scenario consisting of smart energy devices like smart meters, solar panels, industrial IoT sensors, and Home Energy Management Systems (HEMS). It can be simulated using Python with the help of machine learning libraries like TensorFlow, PyTorch.
With the growing demand for secure medical data sharing and accurate insurance premium assessment, privacy, trust, and interoperability have become pressing challenges in healthcare digitalization. Existing solutions often suffer from centralized control, inflexible access policies, and weak privacy guarantees. To address these, we propose a blockchain-based, privacy-preserving scheme that integrates threshold proxy re-encryption and zero-knowledge proofs. Patients’ encrypted medical records are stored in private cloud environments, while access control is enforced through a threshold proxy re-encryption mechanism. Dynamic rekeying and delegation are supported through re-encryption key redistribution. For insurance verification, we generate zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) based on arithmetic circuits that represent the insurance policy requirements. These proofs are verified on-chain without revealing any underlying medical information. Security and performance analyses demonstrate the feasibility and efficiency of the proposed system. Experimental results show that our scheme supports sub-100 ms re-encryption cycles, reduces zero-knowledge proof generation time by over 90% compared to other zk-SNARK schemes, and imposes low on-chain computational overhead. These findings demonstrate the feasibility and potential suitability for real-world privacy-preserving medical data sharing and insurance evaluation.
Graph learning has garnered increasing attention in recent years, which aims to train machine learning models over graph data to support various graph analytic tasks. Coming with the popularity of graph learning are critical privacy concerns regarding the information-rich graphs in many application domains (e.g., finance, social networks, and healthcare). There is thus an urgent call for privacy-preserving graph learning. In this paper, we target an emerging decentralized graph scenario, where a graph is fully decentralized among a set of nodes in such a way that each node only has a limited local view about the global graph. We propose PDGL, a new system framework that can effectively support privacy-assured model training over a decentralized graph, with privacy protection for the links among the nodes as well as the nodes’ private feature data and labels. In contrast to PDGL, prior work does not provide protection for the nodes’ links, feature data, and labels simultaneously. Extensive experiments demonstrate that while providing strong privacy protection for decentralized graph data, PDGL can achieve model utility comparable to the baseline setting of centralized graph learning.
This abstract explores the convergence of Geographic Information Systems (GIS), blockchain technology, and AI in creating Geospatial Smart Contracts (GSCs) for real estate. The research demonstrates how GSCs autonomously verify, execute, and enforce property agreements based on trusted spatial data, addressing challenges in title management, regulatory compliance, and transaction efficiency. Through examination of architectural frameworks, case studies, and implementation challenges including oracle integration and AI validation this chapter reveals that GSCs can reduce transaction times by up to 70% and eliminate fraud. Key findings indicate successful implementation requires standardized geospatial data, robust legal frameworks, and cross-jurisdictional cooperation. This chapter provides actionable insights for developers and policymakers seeking to implement location-aware automated agreements.
What are the key factors determining cryptocurrency prices? This study presents a novel perspective that considers the unique characteristics of the cryptocurrency market. While previous studies have used the value-weighted return of the entire cryptocurrency market as a proxy for the market return, this study demonstrates that Bitcoin (BTC) related features serve as the primary determinant of other cryptocurrencies’ prices. Furthermore, we find that BTC’s own price dynamics are primarily driven by trend-related factors. This finding highlights a fundamental difference in market structure compared to traditional equity markets, where market return as the value-weighted return of the entire stock market is dominant in shaping individual stock prices. To derive these conclusions, this study employs factor analysis using machine learning models such as Random Forest, LightGBM, and Transformer, in addition to a traditional linear predictor, to better capture the complexity of the cryptocurrency market. The findings of this study call for a reconsideration of analytical methods in cryptocurrency pricing and suggest practical implications for BTC-based market analysis and ETF design.
Federated learning (FL) offers a distributed approach for the collaborative training of machine learning models across decentralized clients while safeguarding data privacy. This characteristic makes FL well suited for privacy-sensitive fields such as healthcare and finance. However, addressing the heterogeneity caused by nonindependent and identically distributed (non-IID) data remains a significant challenge for traditional FL methods. To address these issues, the enhancing clustered federated learning with adaptive similarity (AS-CFL) algorithm, which dynamically forms client clusters based on model update similarity and uses a forward-incentive mechanism to improve collaborative training efficiency among similar clients, is proposed in this study. Experimental results on the MNIST and EMNIST datasets reveal that compared with baseline methods such as the CFL, IFCA, and FedAvg models, the AS-CFL algorithm achieves faster convergence—reducing the number of communication rounds by approximately 20%—while maintaining competitive accuracy, demonstrating its effectiveness in heterogeneous FL scenarios.
Sachin Aralikatti, P. Susheelkumar Sreedharan, Akhila K M, J Mexlin · 6 authors
In contemporary democracies, the security, openness, and confidentiality of electronic voting systems are paramount. Due to concerns with scalability and voter privacy, blockchain technology is not yet ready for usage in national-scale elections, despite its immutability and auditability. Using the State Assembly Election as case study, this paper elaborate a e-voting framework that uses Zero-Knowledge Rollups (zk-Rollups) to guarantee scalable, privacy-preserving, and tamper-evident vote recording on the blockchain. Layer-2 zk-Rollup, which is integrated into the architecture with IoT and embedded systems, generates concise cryptographic proofs of vote validity without disclosing individual choices, offloading computationally intensive voting transactions. By validating these proofs on the blockchain, petrol costs are drastically reduced and throughput is greatly improved. Embedded systems also incorporate cryptographic primitives such as zk-SNARKs, Merkle trees, and homomorphic commitments to safeguard against double-voting, end-to-end verifiability, and voter anonymity. This framework provides a viable, secure, and future-proof solution for transparent and scalable e-voting in civic and organisational contexts, it offers a prototype implementation using Zero-Knowledge Rollups on Blockchain with IoT and embedded system integration.
The convergence of blockchain and financial technology (FinTech) is changing the face of finance globally by offering safe, transparent, and affordable services to serve underserved groups of people. The study provides a systematic literature review, covering Payments, Asset Management, Financial Inclusion, and Other Innovations. A bibliometric analysis has the annual publication tendencies indicates the tendency of the increasing academic interest, according to a steep rise of eight articles in 2020 to 35 articles in 2025. The areas of research in Asia and the large journals (Sustainable Finance and World Sustainability Series) support the propagation of knowledge. The analysis of citations demonstrates the work that was foundational in the field of decentralized finance and the AIFinTech symbiosis. The thematic mapping of FinTech and blockchain also points to these two themes as the most important ones, with recent developments of interest in digital identity and regulatory compliance. The international system of cooperation revolves around India, with major collaborations occurring across the continent. These findings can provide researchers and practitioners a mechanism overview of current research dynamics and thematic developments, unlock the inclusive digital finance through blockchain-based Fintech innovations.
The rise of collaborative AI, particularly in distributed Mixture-of-Experts (MoE) systems, has created a critical challenge: how to ensure trust and transparency when aggregating proprietary models from different providers. To address this, we introduce a novel cryptographic protocol ZQ-WMA that enables verifiable and privacy-preserving online learning. Our method integrates zero-knowledge proofs with a quantized version of the Weighted Majority Algorithm, allowing a central aggregator to publicly prove it is honestly combining expert advice and updating weights according to the agreed-upon rules, all without revealing any confidential model parameters.This approach ensures that expert contributions are evaluated fairly and protects valuable intellectual property. Our analysis reveals that the quantization necessary for the zero-knowledge proofs can counter-intuitively enhance prediction accuracy, a phenomenon we attribute to the maximal entropy random walks. Furthermore, our benchmarks demonstrate the efficiency of this method, showing proof generation complexity less than 10% of a standard SHA256 hash function, with O(1) proof size and verification time. This work provides a practical and scalable framework for building trustworthy collaborative AI systems.
Multi-agent systems (MAS) have emerged as a critical paradigm for distributed problem-solving in complex environments. However, their deployment in mission-critical applications faces significant challenges regarding trust, security, and adversarial robustness. This paper presents TrustOrch, a novel dynamic trust-aware orchestration framework designed to enhance the resilience of multi-agent collaboration against adversarial attacks. TrustOrch introduces five key innovations: (1) a dynamic trust assessment mechanism that evaluates agent reliability in real-time using multi-dimensional metrics, (2) an adversary-aware orchestration strategy combining reinforcement learning and game theory to detect and mitigate prompt injection attacks, (3) an adaptive collaboration topology that dynamically adjusts agent communication structures based on task complexity and trust levels, (4) explainable decision tracing for complete audit chains, and (5) a layered security architecture leveraging blockchain technology for decentralized trust verification. Our experimental evaluation demonstrates that TrustOrch reduces collision rates by 62%, achieves 91.7% robustness under adversarial attacks, and reduces communication overhead by 39.8% compared to baseline approaches. The framework achieves robust performance under various adversarial scenarios while maintaining transparency and regulatory compliance, making it particularly suitable for deployment in high-risk domains such as finance, healthcare, and autonomous systems.