The digital economy has grown rapidly with the emergence of blockchain technology, which serves as the foundation for digital assets such as Non-Fungible Tokens (NFTs). NFTs enable unique digital ownership of artworks, music, and other digital assets. However, regulations governing NFTs in Indonesia remain limited, posing legal challenges, particularly in multi-party transactions and ownership protection. This study aims to analyze the regulations governing NFT ownership in Indonesia, identify legal challenges arising in multi-party transactions, and evaluate dispute resolution mechanisms in NFT transactions. Using a normative approach and descriptive analysis method, this research finds that existing regulations do not specifically accommodate the unique characteristics of NFTs, leading to legal uncertainty in ownership and copyright protection. Therefore, more comprehensive and specific regulations are needed to create legal certainty and support the development of the NFT industry in Indonesia.
With the booming development of blockchain, it has gradually gained wide attention in the Internet of Things (IoT), finance, and other fields. However, due to the shared nature of blockchain ledgers among multiple users, sensitive user information, such as transaction amounts and private agreements, can be easily exposed. This poses significant privacy concerns for blockchain users. To address this issue, we propose TrustChain, a high-performance smart contract model based on the Trusted Execution Environment (TEE). TrustChain aims to safeguard the privacy of smart contract codes and user data by leveraging the secure execution environment provided by the TEE. Specifically, we introduce the TEE to run the smart contract with security and privacy without introducing a heavyweight cryptographic algorithm, thus improving the performance of the system. When running smart contracts, the operate nodes equipped with TEE ensure that the Operating System (OS) of the node itself cannot access the data within the TEE. This isolation effectively separates the sensitive information of the smart contract from the external environment. Furthermore, we introduce Verifiable Random Functions (VRFs) to randomly choose the operate nodes to prevent collusion attacks, further improving the security of the model. The graph ledger, based on the Directed Acyclic Graph (DAG), is used to adapt to the high-performance characteristics of a smart contract system based on the TEE. Finally, we simulate the scheme in TrustZone and demonstrate the feasibility of TrustChain through a series of experiments and analyses. The analysis and experimental results demonstrate that our solution exhibits excellent privacy protection performance and achieves higher throughput compared to traditional smart contracts. • We have introduced TrustChain, a smart contract model based on TEE, to ensure the privacy and security of smart contracts. • VRFs are proposed to randomly select operate nodes, preventing collusion and enhancing defense against malicious attacks. • The redesigned consensus mechanism limits blockchain storage to smart contract outputs, preventing leakage of sensitive information. • We enhanced smart contract performance by integrating a DAG-based ledger with TEE's low-latency execution.
Abdullah Ayub Khan, Asif Ali Laghari, Syed Azeem Inam, Sajid Ullah · 6 authors
Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
As a result of IoT-based Wireless Sensor Networks (IoT-WSNs), resource-constrained environments are becoming more efficient and dynamic. Even though IoT-WSNs have many advantages, they also face significant challenges, including their high energy consumption, limited lifespan, and security vulnerabilities. IoT-WSNs for smart cities could be made more energy-efficient and secure by using a blockchain-based approach. Blockchain technology improves energy efficiency and reduces communication costs while ensuring decentralized, secure spectrum management. A smart contract and distributed ledger mechanism reduce redundant data transmissions and facilitate network trust. A blockchain-enabled clustering mechanism allows energy-aware sensing and resource allocation, as well as cognitive radio technology used for efficient spectrum utilization. Compared to existing techniques, the proposed method is more energy efficient, more accurate in sensing spectrums, and more secure. IoT-WSNs provide a solution to energy and security challenges in smart city infrastructure, contributing to sustainable development.
Jinish Patel, Joseph Reiner, Brenden Stilwell, Abdullah Wahbeh · 5 authors
With the growing popularity of cryptocurrencies, detecting potential market manipulation and fraudulent activities has become crucial for maintaining market integrity. In this study, we aim to detect anomalous Bitcoin transactions using an integrated approach by combining clustering techniques with statistical outlier detection. More specifically, anomalies were detected using three approaches: a distance-based method, flagging points with distances greater than the 95th percentile from their cluster centers; a statistical method, identifying transactions with any feature having an absolute Z-score greater than 3; and a hybrid approach, where transactions flagged by either method were considered anomalous. Using sample subset Bitcoin transaction data from 2015, our results showed that the combined approach was able to achieve the best performance with a total of 6492 (6.61%) detected anomalous transactions out of a total of 98,151 transactions.
Climate change is the most pressing global problem, which warrants technological innovation in accurate monitoring and efficient market-based solutions. In this paper, we propose a framework to combine staking with artificial intelligence and blockchain to provide a transparent, secure, and efficient way of monitoring a variety of carbon credits related to carbon footprint. This uses machine learning algorithms to combine satellite imagery, IoT (wearable) data, and immutable blockchain ledgers to create tamper-proof environmental monitoring systems. It suggests brilliant contract architecture that can generate carbon credits through AI to validate the process, federated learning applications to track cross-border emission activity, and neural networks to validate carbon sequestration projects. Using these systems, we achieved orders of magnitude improvement in verification accuracy, transaction transparency, and market efficiency over traditional systems. By employing this integrated approach, some of the most pressing carbon market dilemmas, including narrowing carbon market data integrity issues, delays in verification, and deficits of trust among carbon market participants, can be resolved, and it is a strong foundation for climate action globally.
MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minim MintMart is a decentralized Web3 platform designed for simplifying the process of buying, selling and creating NFTs (Non Fungible Tokens). Currently there are various existing platforms in the market which have features like transparent transactions, auctions in marketplace etc. However they lack some focus in areas of secure transactions, irregular platform fee and royalty compensation for owners which plays an important role in such platforms. The proposed system is designed with an objective to encounter such problems and make the use of NFT marketplace more seamless for the users. The proposed system uses blockchain technology to confront the existing issues. Royalty compensation distribution becomes easier using smart contracts and libraries like OpenZeppelin which helps in better integration of royalty features using its ERC-721 standard. Also MintMart offers fixed minimal platform fee making it more accessible for users The proposed system can successfully support multiple digital formats like images, videos etc. and has also achieved cross chain compatibility allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement. The proposed system can successfully support multiple digital formats like images, videos, etc., and has also achieved cross-chain compatibility, allowing users to interact with various blockchain networks. MintMart plans to expand its features in the near future. These include personalized recommendations, live bidding for NFTs on the marketplace, etc., thus aiming to increase user engagement.
This paper examines how blockchain technology and the Metaverse can address persistent challenges in corporate compliance, with a focus on mitigating criminogenic asymmetries—such as regulatory arbitrage and opacity in cross-border transactions—through decentralized, transparent solutions. By contrasting the U.S. and Italian legal frameworks, we highlight the limitations of retrospective compliance evaluations and propose blockchain-enabled innovations, including immutable audit trails, smart contracts for automated enforcement, and Decentralized Autonomous Organizations (DAOs) to decentralize governance and embed compliance into protocol design. The Metaverse offers a simulated environment for stress-testing compliance protocols against emerging risks, while criminological theories (e.g., global anomie, legal-illegal interfaces) contextualize regulatory gaps in digital economies. We argue that DAOs, as digital-native entities, could revolutionize compliance by replacing hierarchical oversight with algorithmic governance, though challenges like jurisdictional fragmentation and identity verification persist. The study underscores the need for adaptive regulatory frameworks to harness these technologies while balancing transparency, accountability, and privacy.
Abstract- The evolution of blockchain and Web3 technologies has paved the way for decentralized application platforms that enable transparent, tamper-proof transactions without relying on centralized servers. However, existing solutions such as Gitcoin and Giveth are either too complex or unsuitable for modular deployment in educational and lightweight environments. This paper proposes MetaSuite, a role-based, blockchain-driven Software-as-a-Service (SaaS) platform that enables users to create, transfer, and donate tokens while allowing an administrator to securely withdraw funds. Built entirely on the Ethereum blockchain using Solidity smart contracts, MetaSuite operates without a backend and integrates wallet-based authentication via MetaMask and Ethers.js. The platform ensures transparent fund management through on-chain event logging and role-based access controls. Performance evaluations on the Ethereum HoleskyTestnet demonstrate the system’s reliability, gas-efficiency, and real-time responsiveness. By eliminating backend dependencies and emphasizing traceability, MetaSuite serves as a minimalistic yet scalable Web3 solution suitable for academic, experimental, and small-scale real-world deployments. Keywords—Blockchain, Web3, Smart Contracts, Ethereum, MetaMask, Tokenization, SaaS, Ethers.js, Decentralized Applications.
Sarthak Nimje, Rushab Taneja, Om Baviskar, Rachana Patil
Educational institutions face significant challenges with event attendance verification, including manual document validation, fraud risks, and delayed approval processes. This study introduces ElizaEdu, a novel decentralized AI agent system utilizing Ethereum blockchain and ElizaOS to automate and secure attendance verification workflows for academic events. The proposed system integrates autonomous AI agents to handle document validation, approval processes, and ERP integration, while utilizing blockchain technology for immutable record-keeping. The system employs four specialized agents: RequestBot for initial verification, VerifyBot for teacher validation, ApproveBot for department head confirmation, and ERPBot for automatic attendance updates. Through a 3-month pilot implementation with 120 students and 15 faculty members, ElizaEdu demonstrated an 85% reduction in verification time, complete elimination of document fraud, and 84% decrease in administrative workload. The system achieves 97.3% accuracy in document validation and 100% data integrity through blockchain verification. This study presents the architecture, implementation details, and evaluation results, demonstrating ElizaEdu’s effectiveness in transforming attendance management in educational institutions.
Blockchain technology is being increasingly deployed to store and process transactions and information in the global financial sector. Blockchain underpins cryptocurrencies such as Bitcoin and facilitates decentralized finance (DeFi), representing a paradigm shift in the global financial landscape, offering alternative solutions to traditional banking, and fostering financial inclusion. In developing economies such as Morocco, where a significant portion of the population remains unbanked, these digital financial innovations present both opportunities and challenges. This study examines the potential role of cryptocurrencies and DeFi in enhancing financial inclusion in Morocco, where cryptocurrencies have been banned since 2017. However, the public continues to use cryptocurrencies, circumventing restrictions, and the Moroccan Central Bank is now preparing to introduce new regulations to legalize their use within the country. In this context, this article analyses the potential of cryptocurrencies to mitigate barriers such as high transaction costs, restricted access to financial services in rural areas, and limited financial literacy in the country. The study pursues a mixed-methods approach, which combines a quantitative survey with qualitative expert interviews and adapts the Unified Theory of Acceptance and Use of Technology (UTAUT) model to the Moroccan context. The findings reveal that while cryptocurrencies offer cost-efficient financial transactions and improved accessibility, their adoption may be constrained by regulatory uncertainty, security risks, and technological limitations. The novelty of the article thus lies in its focus on the key mechanisms that influence the adoption of cryptocurrencies and their potential impact in a specific national context. In so doing, the study highlights the need for a structured regulatory framework, investment in digital infrastructure, and targeted financial literacy initiatives to optimize the potential role of cryptocurrencies in progressing financial inclusion in Morocco. This underscores the need for integrated models and guidelines for policymakers, financial institutions, and technology providers to ensure the responsible introduction of cryptocurrencies in developing world environments.
Wonkyo Choe, Rongxiang Wang, Afsara Benazir, Felix Xiaozhu Lin
Proto is a new instructional OS that runs on commodity, portable hardware. It showcases modern features, including per-app address spaces, threading, commodity filesystems, USB, DMA, multicore support, self-hosted debugging, and a window manager. It supports rich applications such as 2D/3D games, music and video players, and a blockchain miner. Unlike traditional instructional systems, Proto emphasizes engaging, media-rich apps that go beyond basic terminal programs. Our method breaks down a full-featured OS into a set of incremental, self-contained prototypes. Each prototype introduces a minimal set of OS mechanisms, driven by the needs of specific apps. The construction process then progressively enables these apps by bringing up one mechanism at a time. Proto enables a wider audience to experience building a self-contained software system used in daily life
As digital content distribution expands rapidly through online platforms, securing digital media and protecting intellectual property has become increasingly complex. Traditional centralized systems, while widely adopted, suffer from vulnerabilities such as single points of failure and limited traceability of unauthorized access. This paper presents a blockchain-based secure digital content distribution system that integrates Sia, a decentralized storage network, and Skynet, a content delivery network, to enhance content protection and distribution. The proposed system employs a dual-layer architecture: off-chain for user authentication and on-chain for transaction validation using smart contracts and asymmetric encryption. By introducing a license issuance and secret block mechanism, the system ensures content authenticity, privacy, and controlled access. Experimental results demonstrate the feasibility and scalability of the system in securely distributing multimedia files. The proposed platform not only improves content security but also paves the way for future enhancements with decentralized applications and integrated royalty payment mechanisms.
Abstract Tracing sources and assessing intervention effectiveness are crucial for controlling atmospheric particulate matter (PM) pollution. Isotopic techniques enable precise top-down tracing, but the absence of long-term, global-scale multi-compound isotopic data limits comprehensive analysis. Here, we establish a blockchain-based isotopic database, compiling 34,815 isotopic fingerprints of global PM and its emissions from 1,890 pollution events across 66 countries. This allows retrospective analysis and predictions, revealing that PM sources are distinct, dynamically changing over time, and often asynchronous with interventions. Additionally, we estimate source contributions to PM 2.5 and its compounds, highlighting the increasing impact of biomass burning. Furthermore, projections indicate that by 2100, PM levels may decline to 5.38 ± 0.16 μg/m³ in the Americas and 13.9 ± 1.82 μg/m³ in Asia under climate mitigation scenarios but will still exceed WHO guidelines without further controls on natural emissions. Guiding future interventions with isotopic big data is essential for addressing air pollution challenges.
The integration of blockchain technology into smart city governance frameworks is revolutionizing the way urban management systems are structured, enabling secure, transparent, and decentralized decision-making processes. This chapter explores the transformative potential of blockchain-enabled decentralized governance models in enhancing accountability, reducing bureaucratic inefficiencies, and promoting citizen participation. The application of smart contracts and distributed ledgers is examined as a means to automate governance functions, facilitate real-time resource distribution, and ensure trust among stakeholders. Through detailed case studies and critical analysis, the chapter highlights practical implementations of blockchain in local governments, such as energy trading, public service automation, waste management, and participatory budgeting. In addition, the challenges of citizen engagement, legal compliance, and technological integration are addressed with forward-looking strategies for overcoming these barriers. This chapter contributes a comprehensive understanding of how blockchain can reshape urban governance ecosystems, offering scalable, resilient, and inclusive solutions for future smart cities.
Blockchain technology, with its inherent security, transparency, and immutability, presents a novel approach to addressing critical challenges in public health. This paper explores the potential of blockchain to revolutionize data management, enhance disease surveillance, and empower communities in public health initiatives. We examine how blockchain can secure sensitive health data, facilitate interoperability among disparate systems, and enable decentralized data sharing for research and interventions. Furthermore, we discuss the applications of blockchain in supply chain management for pharmaceuticals, vaccine distribution, and the creation of secure digital identities for individuals. By leveraging blockchain's distributed ledger technology, we can foster trust, improve data integrity, and promote community engagement in public health, ultimately leading to more effective and equitable health outcomes.
This article examines how blockchain technology can revolutionize healthcare data management through enhanced interoperability and automated compliance mechanisms. Healthcare organizations currently face critical challenges with data fragmentation, regulatory adherence, and security vulnerabilities that blockchain architecture addresses through its fundamental characteristics. The decentralized framework creates a secure environment where healthcare stakeholders can exchange information with confidence while maintaining strict privacy controls. Key blockchain components—distributed ledgers, consensus mechanisms, smart contracts, and cryptographic validation—work in concert to enable real-time compliance monitoring, automated audit documentation, and tamper-proof record-keeping. Permissioned blockchain networks prove particularly valuable in healthcare contexts, providing the governance structures necessary for sensitive health information while delivering performance suitable for clinical environments. Implementation case studies reveal tangible benefits including reduced administrative burden, fewer compliance violations, improved data integrity, and faster information exchange between institutions. While healthcare organizations must navigate implementation hurdles such as technical complexity and regulatory uncertainty, the technology demonstrates promising return on investment and positions healthcare providers to meet evolving interoperability standards while strengthening their security posture and compliance capabilities.
Umar Al Faruq, Dwi Fitrizal Salim, Farida Titik Kristanti
This study conducted a large-scale analysis to evaluate the performance of traditional and Markov-Switching GARCH (MS-GARCH) models to estimate the volatility of the top 10 cryptocurrencies by market capitalization. The study compared the performance of the models using goodness-of-fit measures, specifically the Deviance Information Criterion (DIC) and the Bayesian Predictive Information Criterion (BPC). Secondly, we assess the forecasting accuracy for one-day-ahead conditional volatility and Value-at-Risk (VaR). The results obtained show that, in a manner consistent with the findings for the broader cryptocurrency market, the time-varying regime-switching model exhibits superior performance in capturing the complex volatility patterns observed in cryptocurrencies when compared to the traditional GARCH model.
Threshold multi-party fully homomorphic encryption (TMFHE) schemes enable efficient computation to be performed on sensitive data while maintaining privacy. These schemes allow a subset of parties to perform threshold decryption of evaluation results via a distributed protocol without the need for a trusted dealer, and provide a degree of fault tolerance against a set of corrupted parties. However, existing TMFHE schemes can only provide correctness and security against honest-but-curious parties. We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. Furthermore, the scheme implements commitment and non-interactive zero-knowledge (NIZK) proof techniques to verify the TMFHE operations. Finally, our experiments demonstrate that the proposed scheme achieves active security against malicious adversaries. It overcomes the limitation of existing TMFHE schemes that can only guarantee correct computation under passive semi-honest adversaries.
Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or organizations to collaboratively train a shared global model without the need to centralize sensitive data. This decentralized approach addresses growing concerns around data privacy, security, and regulatory compliance, making it particularly attractive in domains such as healthcare, finance, and smart IoT systems. This survey provides a concise yet comprehensive overview of Federated Learning, beginning with its core architecture and communication protocol. We discuss the standard FL lifecycle, including local training, model aggregation, and global updates. A particular emphasis is placed on key technical challenges such as handling non-IID (non-independent and identically distributed) data, mitigating system and hardware heterogeneity, reducing communication overhead, and ensuring privacy through mechanisms like differential privacy and secure aggregation. Furthermore, we examine emerging trends in FL research, including personalized FL, cross-device versus cross-silo settings, and integration with other paradigms such as reinforcement learning and quantum computing. We also highlight real-world applications and summarize benchmark datasets and evaluation metrics commonly used in FL research. Finally, we outline open research problems and future directions to guide the development of scalable, efficient, and trustworthy FL systems.
The development of the world economy, especially in Indonesia, cannot be separated from the element of information technology. The development of information technology will be related to all fields including the financial sector. Cryptocurrency or often referred to as virtual/digital currency is the result of the development of financial technology. Digital currency is starting to be widely used as a means of payment on the internet. The purpose of this currency is to provide convenience and security in payments. With the Blockchain technology in it, it makes transaction costs cheaper. However, the Government in this case Bank Indonesia prohibits transactions using digital/virtual money because it has a dangerous impact on the Financial System, Monetary Stability and Payment System in Indonesia. This study explains the impact of Cryptocurrency on the Indonesian Economy and the government's attitude towards the technology in it. In terms of the technology offered, cryptocurrency is a development of financial technology that allows paper money to be replaced with digital money in financial transactions in the future. It is hoped that the government can study the technology contained in cryptocurrency in more depth so that the policies made later do not prohibit the technology contained in cryptocurrency and provide knowledge to the public to better understand cryptocurrency.
Money laundering with Cryptocurrency in Indonesia in the use of digital currencies provides a loophole for criminals to hide the results of criminal acts through money laundering practices. This article reviews various strategies used in money laundering using crypto assets, with the aim of providing a deeper understanding of this issue. This research applies the normative study method by analyzing legal aspects based on literature as well as the latest developments related to money laundering and cryptocurrencies. Money laundering through crypto assets is carried out in order to disguise the source of illegal funds. Some of the commonly used methods include transactions over the dark network as well as the use of unlicensed mixing services. This crime has been regulated in various laws and regulations that aim to prevent and eradicate the practice of money laundering through cryptocurrencies.