Blockchain technology has completely revolutionized the field of decentralized finance with the emergence of a variety of cryptocurrencies and digital assets. However, widespread adoption of this technology by governments and enterprises has been limited by concerns regarding the technology's scalability, governance, and economic sustainability. This paper aims to introduce a novel hybrid blockchain architecture that balances scalability, governance, and decentralization while being economically viable for all parties involved. The new semi-centralized model leverages strategies not prevalent in the field, such as resource and node isolation, containerization, separation of networking and compute layers, use of a Kafka pub-sub network instead of a peer-to-peer network, and stakes-based validator selection to possibly mitigate a variety of issues related to scalability, security, governance, and economic sustainability. Simulations conducted on Kubernetes demonstrate the architecture's ability to achieve over 1000 transactions per second, with consistent performance across scaled deployments, even on a lightweight consumer-grade laptop with resource constraints. The findings highlight the system's scalability, security, and economic viability, offering a robust framework for enterprise and government adoption.
At present, the existing databases in medical institutions are characterized by singularity and centralization in storage methods. This makes it difficult to guarantee the security, integrity, and traceability of electronic medical data, thus posing a threat to patients' medical privacy. Although some studies have proposed data security storage solutions based on cloud storage and the like, such solutions rely on a completely trustworthy third-party to ensure the reliability of interactions. To address the above-mentioned problems, this paper proposes a decentralized health management data storage mechanism by integrating blockchain technology based on the improved PBFT consensus algorithm. This mechanism uses the improved PBFT consensus algorithm and the optimized Hash encryption algorithm to securely and effectively store health management data in a distributed database, ensuring the integrity and traceability of the data. At the same time, a new data interaction system is designed to prevent direct interaction between the third-party and the database, preventing untrustworthy third-parties from maliciously damaging the data and ensuring data security. In addition, through access control and the Lucene retrieval mechanism, patient privacy is protected and rapid retrieval of health management data is achieved. Experimental analysis shows that compared with algorithms such as Proof of Work (POW) and Delegated Proof of Stake (DPOS), the improved PBFT consensus algorithm endows the medical blockchain system with better stability and throughput. Compared with the ordinary database interaction method, the designed data interaction system effectively prevents direct operations on the database and has good security and anti-tampering properties. Experimental data indicates that the decentralized health management data storage system constructed by integrating blockchain technology based on the improved PBFT consensus algorithm realizes the secure storage, traceability, and anti-tampering of health management data, overcomes the problems of centralized storage, non-traceability, and vulnerability to attacks of health management data, and lays a foundation for the in-depth application and development of blockchain technology in the medical information field.
Joseph T. Yun, Eli Lifton, Eunseo Lee, Y. M. Yun · 11 authors
The rapid advancements in quantum computing present significant threats to existing encryption standards and internet security. Simultaneously, the advent of Web 3.0 marks a transformative era in internet history, emphasizing enhanced data security, decentralization, and user ownership. This white paper introduces the W3ID, an abbreviation of Web3 standard meeting universal digital ID, which is a Universal Digital Identity (UDI) model designed to meet Web3 standards while addressing vulnerabilities posed by quantum computing. W3ID innovatively generates secure Digital Object Identifiers (DOIs) tailored for the decentralized Web 3.0 ecosystem. Additionally, W3ID employs a dual-key system for secure authentication, enhancing both public and private verification mechanisms. To further enhance encryption strength and authentication integrity in the quantum computing era, W3ID incorporates an advanced security mechanism. By requiring quadruple application of SHA-256, with consecutive matches for validation, the system expands the number of possibilities to 256^4, which is approximately 4.3 billion times the current SHA-256 capacity. This dramatic increase in computational complexity ensures that even advanced quantum computing systems would face significant challenges in executing brute-force attacks. W3ID redefines digital identity standards for Web 3.0 and the quantum computing era, setting a new benchmark for security, scalability, and decentralization in the global digital twin ecosystem.
Khalid Khan, Adnan Khurshid, Javier Cifuentes‐Faura
Abstract This study uses the Bayesian structural model to assess the causal effect of the futures exchange (FTX) insolvency on cryptocurrencies from October 2022 to December 14, 2022. Findings show that FTX insolvency negatively impacts cryptocurrencies. Moreover, the results indicate rapid divergence from counterfactual predictions, and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse. Cryptocurrency is reacting strongly to the uncertainty caused by insolvency. In relative terms, the collapse of FTX has been highly detrimental to Solana and Ethereum. Furthermore, the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred. FTX collapsed owing to a mismatch between the assets and liabilities. The industry is still mostly unregulated, and regulators must act quickly, highlighting the need for outstanding innovation and decentralized and trustless technology adoption.
Said Hamisi Said, Ramadhani Sinde, Efraim M. Kosia, Mussa Ally Dida
The prevalence of fake educational credentials poses a threat to the meritocratic nature of the education system and job markets. Verification of certificates to combat forgery has been a challenging endeavor due to the weaknesses of the current methods. Blockchain, capitalizing on its unique attributes, can provide an optimal solution to certification and verification problems by ensuring disintermediation, immutability, tamper-proof, efficiency, and security. Efforts to explore its potential in addressing these problems continue to gain momentum. However, the existing blockchain-based initiatives do not offer a holistic solution to the forgery problem, as they solely focus on a single education level or institution. Furthermore, these initiatives lack the essential features required to fully address this problem. This paper proposes a comprehensive blockchain-enabled system for issuing certificates from different educational levels and institutions in the country, providing a one-stop center for verifiers, such as employers, to verify all certificates a candidate possesses. As a proof of concept, a decentralized application (DApp), ElimuChain, has been developed, utilizing smart contracts and the InterPlanetary File System (IPFS). The system is deployed on the Binance Smart Chain (BSC) blockchain to evaluate its applicability in addressing the problem in the Tanzanian context. The results demonstrate that the proposed solution successfully manages the certification and verification process, and it is cost-effective, scalable, and efficient. Moreover, its performance was compared with the previous solutions in terms of latency and throughput. The comparison results show that it performs better than the counterpart for transactional operations.
With the increasingly turbulent political situation and the outbreak of public health events without warning, it will not only affect people’s physical health, but also affect the global financial market, causing the market to fall into a huge crisis, thus leading to a continued decline in the worldwide economy. During periods of financial market turmoil, many investors fall into panic and urgently need a “haven” to protect their assets. With the rise of the digital economy, gold no longer seems to be the only safe-haven option. Bitcoin has gradually entered the investors’ field of vision. Some investors believe that Bitcoin can become an emerging safe-haven asset that is as important as or surpasses gold. Based on an analysis of the safe-haven properties of Bitcoin and gold during major political and historical events and public health events, this article will clarify which of the two is more suitable as a reliable contemporary safe-haven asset and provide advice to investors.
In today’s highly interconnected digital environment, computer network information security faces multiple threats such as data breaches, identity forgery, and malicious attacks. As a decentralized and tamper-proof distributed ledger system, blockchain technology provides a new technical path for data security through its core features of cryptographic algorithms and consensus mechanisms. This technology can ensure the integrity of information during data transmission and storage, effectively enhance the overall credibility of network systems, and inject new vitality into the information security protection system. Based on this, this paper explores the implementation of blockchain technology in computer network information security.
Blockchain technology has emerged as a transformative innovation, redefining industries through its decentralized and secure framework. Smart contracts—self-executing code deployed on blockchain platforms like Ethereum—enable decentralized applications (dApps) to automate processes across finance, healthcare, and supply chain management. However, their programmability introduces significant security risks, making them susceptible to vulnerabilities that can be exploited by malicious actors. While different detection methods have been developed to address these security concerns, they often remain inadequate. Traditional approaches to smart contract vulnerability detection, such as static and dynamic analysis, are limited by their reliance on predefined rules, making them ineffective for addressing complex, domain-specific vulnerabilities in rapidly evolving decentralized ecosystems. This thesis addresses these challenges by leveraging Large Language Models (LLMs), which have demonstrated exceptional capabilities in contextual understanding and reasoning. Through parameter-efficient fine-tuning techniques, including Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA), the research enhances the scalability and accessibility of LLMs for vulnerability detection. The study also examines Retrieval-Augmented Generation (RAG) frameworks to dynamically retrieve and process relevant information. The research develops and evaluates two distinct approaches: fine-tuning LLMs and RAG. The CodeGemma 7B model achieved exceptional results, attaining 94.78% accuracy on the DeFi Hacks & Top200 dataset and 92.52% on the TrustLLM dataset, surpassing previous benchmarks using larger and proprietary models. The best-performing RAG model, Gemma 2, achieved 79.1% accuracy, demonstrating the effectiveness of retrieval-based augmentation. These contributions lay the groundwork for more scalable, efficient, and democratized tools for securing blockchain ecosystems, addressing the limitations of traditional methods while offering cost-effective solutions for safer decentralized systems.
B a c k g r o u n d . The study addresses the rapid expansion of data processed within distributed digital ecosystems, where traditional centralized storage models introduce risks due to single points of failure and limited transparency in monitoring changes. The need is emphasized for secure, resilient, and verifiable mechanisms capable of protecting sensitive information in dynamic multi-user environments. M e t h o d s . A hybrid blockchain architecture integrating public and private ledgers is developed. A mathematical model of decentralized data protection is formalized, digital signatures, smart contracts, and cryptographic hashing are applied, and a functional prototype is implemented using Hyperledger Fabric with the RAFT consensus algorithm to validate secure access and ensure transaction integrity. R e s u l t s . Analytical modeling and simulation experiments involving networks of 10–100 nodes demonstrate increased system resilience by approximately 20–25% while maintaining stable transaction latency. The implemented model reliably detects unauthorized access attempts and modification actions and shows compliance with international standards, including ISO/IEC 27001 and GDPR. C o n c l u s i o n s . The findings confirm that blockchain-based architectures can significantly enhance data security in distributed environments and surpass traditional centralized protection models. The proposed framework ensures integrity, transparency, and traceability with minimal performance degradation, making it suitable for financial, governmental, medical, and corporate systems. Further development is considered promising in the context of integrating machine learning, quantum resistant cryptography, and cloud infrastructures.
Smart contract security is a critical concern in the blockchain ecosystem, as vulnerabilities have resulted in billions of dollars in financial losses. This urgency has driven the development of numerous automated security tools; however, their effectiveness is tightly linked to the data on which they are trained and evaluated. In current research practice, datasets vary widely in structure, provenance, and quality, as they are often manually assembled from various sources to satisfy the specific needs of individual studies. Because obtaining verified, real-world vulnerabilities and exploits is challenging, many researchers supplement or replace real data with artificially injected or otherwise synthetic examples. These practices, collectively, lead to evaluation settings that do not fully capture the complexity, diversity, and exploitability of vulnerabilities found in contracts intended for real use. As a result, tool performance is frequently overestimated in academic benchmarks, contributing to a persistent gap between reported results and the practical needs of auditors and developers. This thesis addresses this gap by introducing PoPoC, a novel benchmark dataset built from real-world, verified Proof-of-Concept (PoC) exploits. We present a reproducible workflow for creating this dataset, which begins by scraping 4,770 audit reports from the Solodit platform, filtering for 1,053 reports that contain dedicated PoC sections. These candidates are then automatically enriched with quality metrics using a Large Language Model (LLM) and ranked via a custom priority-scoring heuristic. The core of this work involved a rigorous manual validation of the top 100 ranked audits. This process resulted in a curated dataset of 58 fully reproduced, executable exploits, with each reproduction packaged within a containerized environment to ensure reliability. Our analysis confirms that 100% of the entries in the PoPoC dataset are technically correct. However, we also found that the original PoCs often have inconsistent test oracle coverage (with only 28 of 58 having complete assertions) and that the dataset shows limited platform diversity, being sourced primarily from Code4Arena. The primary contributions of this thesis is the reproducible method to extract and validate exploit PoCs from raw audits, the curated PoPoC dataset, and its accompanying codebase containing PoC reproductions and the vulnerable source code. This work provides the first benchmark to systematically connect formal vulnerability descriptions and vulnerable source code with manually verified, proven, and runnable PoC exploits. PoPoC serves as a high-quality, reproducible foundation for benchmarking security tools, training auditors, and advancing future research in automated vulnerability detection.
As the core component of blockchain applications, smart contracts are increasingly scrutinized for their security. Among various vulnerabilities, infinite loop flaws pose significant threats due to their hidden nature and potential for exhausting system resources. This paper proposes a static detection method based on Graph Convolutional Networks (GCNs), which transforms smart contracts into control flow and data flow graphs. Through graph-based modeling and vectorized encoding, semantic features such as loop structures and function dependencies are effectively captured. An improved GCN architecture is employed to identify potential infinite execution patterns through neighborhood aggregation and graph-level representation learning. Experimental results demonstrate that the proposed method achieves high accuracy and F1-score across real-world contract datasets, offering an effective and scalable solution for smart contract vulnerability analysis.
ABSTRACT Cryptocurrency taxation poses a fundamental dilemma: how to ensure compliance while protecting privacy and enabling real‐time cross‐border coordination. This paper introduces a blockchain‐driven framework to address these challenges. First, a permissioned consortium chain with a multi‐channel architecture links OECD tax authorities, compliant exchanges and international organizations, safeguarding data sovereignty. Second, a dynamic account‐transaction graph with rule‐guided subgraph templates detects hidden ‘tax‐base dark matter’ behaviours, including mixing services, cross‐chain transfers and NFT profit masking. Third, a zero‐knowledge proof protocol (zero‐knowledge‐TaxProof) encodes tax rules into verifiable arithmetic circuits, allowing taxpayers to prove taxable conditions without exposing details. Fourth, a dynamic‐weight PBFT mechanism ties node voting power to data integrity, accuracy and responsiveness, enabling multinational collaboration. Fifth, off‐chain identity anchoring with on‐chain KYC decoupling preserves privacy while permitting traceability strictly under judicial authorization. Finally, a real‐time dashboard and adaptive early‐warning system monitors global tax‐base changes with sub‐minute responsiveness. Experiments on a Hyperledger Fabric testbed show the model achieves 87.8% identification accuracy, an average dark‐matter capture rate of 88.9%, leakage entropy of 2.3 bits and event confirmation within 53 s. These results demonstrate a feasible, sustainable paradigm for reconstructing global digital tax governance that balances privacy, compliance and efficiency.
Integrating blockchain with IoT ensures secure, transparent data exchange through immutability and consensus mechanisms, preventing data tampering. However, the increasing number of IoT devices raises risks like unauthorized access and network attacks. Blockchain scalability issues also affect throughput and latency, challenging real-time IoT applications. This thesis addresses these challenges through four contributions that aim to improve the security, scalability, and efficiency of blockchainbased IoT networks, balancing security with performance needs. Our first contribution is to develop an end-to-end security mechanism for IoT networks, called the trust-based ABAC mechanism for IoT networks (TABI). TABI integrates edge computing and blockchain technology to mitigate risks from malicious devices and offload computational tasks to edge layers. It operates on Hyperledger Fabric (HLF), a permissioned blockchain that enhances throughput and latency through its executeorder- validate architecture. Our second objective is to provide scalability within blockchain-based IoT networks using a sidechain-based trust and access control system, named sidechain-based trust and access control mechanism for IoT networks (SATI). By distributing trust evaluation and access control operations across a separate blockchain or sidechain, SATI improves the scalability of IoT networks. We implement a cross-chain transfer mechanism to ensure communication between the sidechain and the mainchain, thus overcoming a fundamental limitation of traditional blockchain architectures. Our third contribution is to improve the security of the IoT network by introducing a Zero-Knowledge Proof-based Mutual Authentication (ZPMA) mechanism, a privacy-preserving mutual authentication mechanism. Utilizing Zero-Knowledge Proofs (ZKP) based on the quadratic residue technique, Z-PMA ensures secure and private mutual authentication between edge devices and IoT devices. We also implement an incentive mechanism to select additional authenticators from the base station layer to reduce authentication latency and support the demands of low-latency IoT networks. Our fourth contribution is to detect and resolve conflicting transactions in HLF-based IoT networks at an early stage, known as the early-stage conflict transaction resolution (ECR) mechanism. ECR identifies and resolves conflicting transactions at an early stage using a local cache at the endorsement phase of the HLF transaction processing. Additionally, ECR uses dependency model and an efficient reordering process to distribute transactions in a way that minimizes conflicts. This mechanism enhances the performance of HLF-based IoT networks by reducing the impact of conflicting transactions, ultimately improving throughput and latency.
Chachar B., Cavazza M., Bracciali A., Ferrara P. · 5 authors
Smart Contracts are at the heart of blockchain transactions, and their integrity is essential to blockchain reliability and performance. Specific errors in Smart Contract code are known to trigger vulnerabilities, which have been categorized into different patterns. Following the success of Large Language Models in software analysis, several authors have proposed the use of LLM to detect Smart Contract vulnerabilities. In this paper, we compare the performance of various LLM as well as formal analysis tools in analyzing Ethereum Smart Contracts for various vulnerabilities, based on standard datasets. Unlike previous work that used LLM fine-tuning, we explore performance based on direct In-Context Learning using standard Prompt Engineering techniques. Our results suggest that the straightforward use of LLM may still be beneficial in the analysis of Smart Contracts, depending on the vulnerability type.
Cyber-physical-social (CPS) systems require searchable encryption (SE) to safeguard sensitive data before storing it in the cloud. Existing dynamic searchable symmetric encryption (DSSE) methods have problems with index creation, document updates that lose data, and search speed. These issues must be addressed to develop an efficient CPS system. Therefore, we have developed a new approach that integrates a DSSE protocol with a blockchain-based data management system, thereby establishing a secure and efficient method for managing encrypted data. We make sure that past data remains private and that we can quickly search through data by building a forward index with a special pseudorandom function (PRF) and checking it with symmetric encryption. Keeping the encrypted index on a private distributed ledger and the secret data on a public ledger reduces storage and speeds up transaction processing. To enhance data privacy and access verification, an additional authentication system must prevent unauthorized access to the private blockchain. Authorization systems verify access permissions and execute the outcomes. Performance evaluation shows that the proposed solution improves the integrity of encrypted data and the speed of queries in the Chicago Crime and Enron Email datasets. The proposed method used only 0.68 MB client and 121.4 MB servers, builds in 121.4 s, updates in 156.4 for client and 167.2 ms for server, and outperforms all in speed, storage, and efficiency. The results are also checked for correctness and originality at the same time to reduce the complexity and computational cost by 60% and 70%, respectively, for modern cyber-physical social applications. Finally, the proposed method improves existing methods based on an extensive evaluation of Chicago Crime and Enron Email datasets, and can benefit modern CPS systems.
V. S. Ramachandran, Hayder M. A. Ghanimi, Bhaskar Marapelli, Navleen Kaur · 8 authors
This paper introduces a Cyber-Physical System (CPS) that can be used to improve privacy protection in enterprise Blockchain (BC) systems, particularly in Hyperledger Fabric (HF). The proposed CPS employs advanced methods to facilitate a privacy-preserving authorization mechanism. Using data masking, Homomorphic Encryption (HE), and complex digital signatures, the model ensures the confidentiality of transaction data during the authorization process. Additionally, the implementation of Secure Multiparty Computation (SMC) and Zero-Knowledge Proofs (ZKP) enhances the security of the data by preventing unauthorized personnel from accessing it and ensuring that the approving peers remain anonymous. The solution proposed in the paper addresses the problems of conventional centralized access control, which can be manipulated and has data leakage problems. Experimental results have proved the design’s practical applicability and security trade-off, providing a robust foundation for enterprises to adopt privacy-preserving BC. Thus, the HF platform integration demonstrates the model’s real-world applicability, which developed a secure, scalable, and efficient solution for handling sensitive transactions in distributed networks.
Information Communication Technologies, Deniz Salucu, Tunga Sayıcı, Information Communication Technologies
The rapid rise of adoption of digital identity presents a transformative opportunity to eliminate resource-intensive physical identity systems, no longer requiring printed cards, plastic credentials, or in-person office visits, thereby substantially reducing carbon footprints. This paper introduces a comprehensive Self-Sovereign Identity (SSI) model built upon Hyperledger Indy, enabling end users to securely store and manage their identities directly on personal devices. We explore two different enrollment methods: QR code-based digital credential issuance and NFC-powered chip-based identity verification, further enhanced through zero-knowledge proofs and verifiable credential protocols. The ecological advantages are emphasized through the eradication of physical identity artifacts and associated administrative processes, advancing the field of green digital technologies in support of ecological preservation and sustainable identity infrastructures.
Seyyit Murat Seçilmiş, Danışman: Prof.Dr. İsmet Çavuşoğlu
Oyun, geçmişten günümüze eğlencenin en önemli unsurlarından biri olmuştur. Sanal bir dünya ile alternatif evrenler sunan oyunlar, kendi içlerinde yarattıkları ekonomi sayesinde gerçek dünya ile maddi temas sağlayabilmektedir. Bu noktalardaki para kazanma yöntemlerinden biri de oyun içi kostüm, eşya, ikon ve benzeri edinimlerin gerçek dünya parası ile satılması ve gerçek dünyada bir varlık yaratılmasıdır. Blockchain teknolojileri sayesinde bu varlıklar son derece güvenli bir şekilde merkezsiz olarak saklanabilmektedir. Şifreleme yöntemleri ve dağıtık defter yapısı sayesinde bu teknoloji, kullanıcıların dijital varlıklarını üçüncü taraflara ihtiyaç duymadan güvenli bir ortamda saklamalarına olanak sağlıyor. Blockchain sadece oyun sektöründe değil, finans, sağlık, lojistik, eğitim ve daha birçok alanda devrim niteliğinde yenilikler sunan teknolojik bir gelişmedir. Özellikle verilerin değişmez ve şeffaf bir şekilde saklanmasını sağlayarak güvenilirliği artırıyor ve aracılara olan bağımlılığı azaltıyor. Bu sayede blockchain, dijital ekonomiden sosyal sistemlere kadar geniş bir alanda insan hayatını derinden etkileyen ve yeniden şekillendiren bir unsur haline gelmiştir. P2E (Play to Earn), oyunlarda oynadıkça kripto varlıkları kazanmaya yönelik bir modeldir. Oyuncunun oyun oynama süresini kazanarak artırmayı hedefler. Bu da oyuna bağlı kripto varlıkların değerini artırır ve bir ekonomi yaratır. Benzer şekilde, NFT (Non-Fungible Token) kazanıp satarak, blockchain teknolojisi oyunlarda gerçek dünya kazançları elde edilmesini sağlar. Dijital varlıkların gerçek dünyadaki karşılığı yüksek güvenlik önlemleri gerektirir. Blockchain teknolojisi burada ana aktörlerden biridir. Oyun geliştirme platformlarında blockchain tabanlı ödeme sistemlerinin güvenliği, işlem bütünlüğünü, kullanıcı gizliliğini ve varlık sahipliğini artırmaya odaklanan kritik bir araştırma alanıdır. Blokchaini teknolojisi, veri tahrifatı ve yetkisiz erişim gibi geleneksel ödeme sistemleriyle ilişkili riskleri azaltan merkezi olmayan bir çerçeve sunar. Bu çalışmada model mimari olarak Solana blockchain altyapısı entegre edilmiştir. Solana'nın yüksek hızlı ve düşük maliyetli işlem özelliklerinden yararlanılarak projenin temel gereksinimlerini karşılayacak bir sistem tasarımı gerçekleştirilmiştir. Akıllı sözleşmeler Rust programlama dili kullanılarak geliştirildi. Blockchain tabanlı ödeme sistemlerinin güvenilir ve verimli çalışmasını sağlamak için Rust'ın performans odaklı ve güvenli bellek yönetimi özellikleri tercih edildi. Akıllı sözleşme geliştirme sürecinde işlem mantığı, veri doğrulama ve güvenlik önlemleri gibi unsurlar özenle tasarlandı. Ayrıca token transfer fonksiyonları ve kullanıcı yetkilendirme süreçleri Solana'nın Program Library standardı kullanılarak modellendi. Web tabanlı kullanıcı arayüzü React kütüphanesi kullanılarak oluşturuldu. React'in bileşen tabanlı mimarisi ve dinamik veri yönetimi yetenekleri, kullanıcı deneyimini geliştirecek bir arayüz geliştirilmesini sağladı. Bu süreçte kullanıcıların dijital cüzdanları üzerinden sisteme güvenli bir şekilde erişebilmeleri için dijital cüzdan entegrasyonu gerçekleştirildi. Kullanıcıların blockchain işlemlerini kolayca gerçekleştirebilmelerini sağlayan Phantom, Sollet vb. gibi Solana uyumlu cüzdanlar tercih edildi. Sistem bileşenleri detaylı olarak analiz edilmiş ve mimari tasarımın temel taşları olarak belirlenmiştir. Bu bileşenler arasında akıllı sözleşmeler, blokchaini ağı, kullanıcı arayüzü ve dijital cüzdanlar yer almaktadır. Ayrıca bu bileşenler arasındaki iletişim mekanizmaları tanımlanmış ve veri akışını optimize etmek için gerekli protokoller belirlenmiştir. Sistem, kullanıcıların işlem güvenliğini ve veri bütünlüğünü sağlamayı amaçlayan bir yapıda tasarlanmıştır. Bu yöntem ve araçlar bir araya getirilerek, blockchain tabanlı ödeme sistemlerinin oyun geliştirme platformlarında uygulanabilirliğini değerlendiren bir model sunulmuş ve proje güvenlik açısından tartışılmıştır. Bu tez, akıllı sözleşmenin Rust ile ilgili güvenlik, giriş ve çıkış işlemleri, kullanıcı ve imzalayan kimliklerinin güvenli çalışması gibi güvenlik açıklarını ve avantajlarını inceleyerek oyun platformlarında blokchaini tabanlı ödeme sistemlerinin güvenliğine genel bir bakış sunmaktadır.
Luigi Pavarini de Lima, Liliam Sayuri Sakamoto, Jair Minoro Abe, Jonatas Santos De Souza · 8 authors
The objective of this article is to propose a research structure to optimize this security of assets with NFT - Non- fungible Token with the use of DLP - Data Loss Prevention and Paraconsistent Logic for the identification not only preventively, but actively of the loss, theft, misuse and leakage of this type of assets during their use in the Metaverse. A bibliographic review was carried out on Metaverse, DLP, Paraconsistent Logic, Artificial Intelligence techniques [10][27][28], NFT [49][50], and Data Protection [4] with a focus on the LGPD (Brazilian Data Protection Law) [2][23], in conjunction with exploratory research. With a DLP and a database provided by the transport company with 200 articles analyzed. It was verified that a significant amount of data would be discarded in the first stage of the process (37%) since they do not present an active definition on the status of these assets. Considering the growing technological innovation with the use of the Metaverse, as an environment for educational, business and governmental interaction against the risk of cyber-attacks, there is an urgent need to strengthen its security, even more so when in this environment, where there is the possibility of moving assets with NFTs that are objects of great value acquired and traded in this medium. With the use of the Python program in the DLP, it was observed that it presented a 37% data loss in its analysis with this Artificial Intelligence [11] process only with the performance of the DLP, compared to the optimization of this analysis with the use of Paraconsistent Logic at 23%, that is, a use of more than 15% of the data.