With the rapid evolution of the Decentralized Web (DWeb), decentralized technologies have paved new avenues for Web3 applications and the authentication of digital assets. Among them, Non-Fungible Tokens (NFTs) have gained significant popularity due to their immutability and uniqueness, reshaping the landscape of artistic creation, marketing, and intellectual property protection. However, current blockchain-based NFT implementations still face core challenges within decentralized architecture: how to maintain decentralization while ensuring the visual uniqueness of digital assets and reducing storage costs. The rampant issue of duplication undermines the scarcity of digital art and erodes market confidence in copyright authenticity. Moreover, high gas fees and energy consumption further hinder the widespread adoption of NFTs, while reliance on external storage solutions like InterPlanetary File System (IPFS) introduces risks of data instability and loss. To address these challenges, this article presents the UniqueNFT framework, a novel architecture that deeply integrates blockchain oracles with decentralized storage verification mechanisms. The framework achieves three key technological breakthroughs: Using image inversion and generation techniques based on Encoder for Editing (E4E) and StyleGAN3, it extracts compact and expressive semantic features from NFT images, enabling efficient data compression and significantly reducing on-chain storage volume; The Crypto-Mask algorithm, by utilizing the hash value of blockchain user information (user-controlled SHA-256 digest of Ethereum address, user nickname, and registration time), ensures the visual uniqueness of NFTs; A smart contract extension compatible with the ERC721 standard, demonstrating UniqueNFT’s seamless integration within the blockchain ecosystem. By leveraging the technologies of the Decentralized Web, our framework represents an important step forward in enhancing the security and uniqueness of digital assets. It not only innovatively resolves the issues of NFT duplication and homogenization but also injects new vitality and long-term momentum into the creation of a trusted, sustainable blockchain-based digital asset ecosystem.
Cross-chain technology enables interoperability among otherwise isolated blockchains, supporting interactions across heterogeneous networks. Similar to how multi-hop communication became fundamental in the evolution of the Internet, the demand for multi-hop cross-chain interactions is gaining increasing attention. However, this growing demand introduces new security and privacy challenges. On the security side, multi-hop interactions depend on the availability of multiple participating nodes. If any node becomes temporarily offline during execution, the protocol may fail to complete correctly, leading to settlement failure or fund loss. On the privacy side, the need for on-chain transparency to validate intermediate states may unintentionally leak linkable information, compromising the unlinkability of user interactions. In this paper, we propose the Cross-Chain Channel Network (CCN), a decentralized network designed to support secure and privacy-preserving multi-hop cross-chain transactions. Through experimental evaluation, we identify two critical types of offline failures, referred to as active and passive offline cases, which have not been adequately addressed by existing solutions. To mitigate these issues, we introduce R-HTLC, a core protocol within CCN. R-HTLC incorporates an hourglass mechanism and a multi-path refund strategy to ensure settlement correctness even when some nodes go offline during execution. Importantly, CCN addresses not only the correctness under offline conditions but also maintains unlinkability in such adversarial settings. To overcome this, CCN leverages zero-knowledge proofs and off-chain coordination, ensuring that interaction relationships remain indistinguishable even when certain nodes are temporarily offline.
This research paper presents the design, architecture, and evaluation of a Hybrid Blockchain Database System, a decentralized–distributed model that integrates blockchain’s immutability with the high-performance data processing capabilities of traditional database systems. The proposed hybrid architecture addresses key limitations of fully on-chain storage—such as scalability constraints, high transaction costs, and limited query efficiency—by combining on-chain verification layers with off-chain distributed storage engines. The system introduces a multi-layered architectural model consisting of: (1) a blockchain consensus and validation layer for ensuring trust and tamper-resistant auditability; (2) a distributed database layer for high-speed read/write operations; (3) an interoperability layer that synchronizes state between on-chain and off-chain data; and (4) an IPFS/Filecoin-based decentralized storage framework for storing large datasets and metadata. This design enables secure state anchoring, cryptographic data integrity, and scalable data indexing across heterogeneous storage environments. A detailed performance evaluation is conducted using a combination of Ethereum-compatible smart contracts, IPFS storage, and distributed NoSQL/SQL databases. Key metrics such as transaction throughput, read/write latency, storage overhead, and consistency verification time are analyzed under varying workloads. Experimental results demonstrate that the hybrid system improves data scalability by 70–85%, reduces storage costs by over 90%, and maintains strong auditability through periodic blockchain state commitments. Security analysis is performed using static and dynamic testing tools to identify common vulnerabilities such as broken access control, inconsistent state updates, or missing verification boundaries, ensuring robustness of the hybrid synchronization logic. Additionally, the paper discusses design trade-offs involving decentralization vs. performance, on-chain transparency vs. gas efficiency, and consistency vs. latency across distributed components. This work provides a comprehensive blueprint for the development of real-world hybrid blockchain database systems suitable for supply chain management, enterprise data systems, government registries, financial applications, and Web3 infrastructure. The results highlight how hybrid architectures can bridge the gap between decentralized trust and scalable data management in next-generation digital ecosystems.
Blockchain technology has fueled exponential growth across various industries, including finance, supply chain management, and healthcare, enabling greater transparency in transaction management and supporting decentralized implementations. This paper presents a comprehensive performance analysis of permissioned and permissionless blockchain platforms, specifically Hyperledger Fabric and Ethereum. The study evaluates these platforms with varying transaction workloads (100 to 1000 transactions) with a consistent network. Our objective is to measure key performance metrics such as send rate, throughput, latency, resource utilization, and transaction success rate using established benchmarking tools and methodologies. The findings offer valuable insights into the comparative strengths, limitations, and optimal use cases of these blockchain platforms across different performance parameters. The results indicate that Hyperledger Fabric achieves, on average, 3.5–4.5 times higher throughput and 10–12 times lower latency than Ethereum, while consuming 2.5–3 times less memory across tested workloads. In contrast, Ethereum demonstrates a higher send rate and lower CPU demand in some operations. Overall, the study suggests that Hyperledger Fabric is better suited for enterprise applications that demand high scalability and performance.
The Byzantine Generals Problem, introduced by Lamport, Shostak, and Pease, fundamentally addresses how a distributed system can achieve consensus even when some of its components are unreliable or malicious. This paper delves into the mathematical bounds that govern the solvability and efficiency of Byzantine Agreement (BA) protocols, specifically exploring the role of network topology in these limits. We examine classical impossibility results, such as the n $>$ 3f requirement for unauthenticated synchronous systems and the Fischer-Lynch-Paterson impossibility for deterministic asynchronous systems. Furthermore, we introduce "treasonous topologies" as a conceptual framework to systematically analyze how graph-theoretic properties like connectivity and diameter influence the minimum number of honest nodes required, message complexity, and time complexity. Special attention is paid to authenticated protocols which can relax certain bounds by employing digital signatures. This study elucidates the intricate relationship between adversarial capabilities, network structure, and the inherent mathematical constraints on achieving robust agreement in the presence of malicious nodes. We also touch upon modern applications in blockchain and distributed ledger technologies, where these theoretical bounds translate into practical considerations for security, scalability, and decentralization. A core contribution of this work is the synthesis of these established bounds with a detailed examination of how varying network structures fundamentally dictate protocol design and performance, offering a clearer lens through which to understand the vulnerabilities and strengths of real-world distributed systems.
Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months (since late 2024), we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes (e.g., pings and protocol-level handshakes) to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.
To address the limitations of blockchain data storage capacity and uneven dis-tribution, this paper proposes a Chord dual-ring distributed storage method based on virtual nodes. Building upon the original Chord protocol, this approach introduces virtual rings to construct a “storage ring-virtual ring” dual-ring structure. Target virtual nodes are located through routing table lookups, and data is distributed across the storage ring via a name mapping mechanism. Sim-ulation experiments validate the proposed scheme's effectiveness by evalu-ating load balancing and query success rate across varying sharding granulari-ties. Results demonstrate that this approach not only efficiently achieves shard-ed storage for blockchain data but also ensures balanced distribution of block data.
This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally models such problems and propose two distinct problem reductions to standard MDPs. One problem reduction provides a novel, counter-intuitive perspective, and combining the two problem reductions enables significant improvements in dynamic programming algorithms such as value iteration. In the case of the LRA which size is parameterized by $κ$ (in Ethereum's case $κ$= 325), we reduce the computational complexity from $O(2^κκ^{2^{κ+2}})$ to $O(κ^4)$ (per iteration). This solution also provide the usual benefits from Dynamic Programming solutions: exponentially fast convergence toward the optimal solution is guaranteed. The dual perspective also simplifies policy extraction, making the approach well-suited for resource-constrained agents who can operate with very limited memory and computation once the problem has been solved. Furthermore, we generalize those results to a broader class of MDPs, enhancing their applicability. The framework is validated through two case studies: a fictional card game and the LRA on the Ethereum random seed consensus protocol. These applications demonstrate the framework's ability to solve large-scale problems effectively while offering actionable insights into optimal strategies. This work advances the study of MDPs and contributes to understanding security vulnerabilities in blockchain systems.
Chung, Siu Kei, Francisco Carpio, Andrei Navoichyk, Siarhei Valasovich · 11 authors
The internet faces a sovereignty crisis due to power concentration and data growth among a few hyperscalers, leading to centralization and loss of user control. This consolidation risks censorship and creates single points of failure. While Web3 offers decentralized solutions, they often sacrifice either scalability, decentralization, or security, which are key elements in the blockchain trilemma. These solutions also struggle with limited access to enterprise-grade hardware and frequently rely on centralized infrastructure. The Impossible Cloud Network (ICN) addresses these issues by creating a multi-tiered, decentralized infrastructure layer. ICN offers a composable service layer, an enterprise-grade hardware resource layer, and a transparent, permissionless HyperNode network for performance enforcement. By strategically decoupling and decentralizing each layer, ICN aims to provide an open, extensively scalable infrastructure that ensures digital sovereignty, eliminates single points of trust, enables service programmability, and offers a decoupled architecture for limitless possibilities in the future internet.
Ivan Homoliak, Martin Perešíni, Marek Tamaškovič, Timotej Ponek · 6 authors
Proof-of-Stake (PoS) consensus protocols often face a trade-off between performance and security. Protocols that preelect leaders for subsequent rounds are vulnerable to Denial-of-Service (DoS) attacks, which can disrupt the network and compromise liveness. In this work, we present PoS-CoPOR, a single-chain PoS consensus protocol that mitigates this vulnerability by integrating a native onion routing mechanism into the consensus protocol itself. PoS-CoPOR combines stake-weighted probabilistic leader election with an anonymization layer that conceals the network identity of the next block proposer. This approach prevents targeted DoS attacks on leaders before they produce a block, thus enhancing network resilience. We implemented and evaluated PoS-CoPOR, demonstrating its ability to achieve a throughput of up to $110 \mathrm{tx} / \mathrm{s}$ with 6 nodes, even with the overhead of the anonymization layer. The results show that native anonymization can provide robust DoS resistance with only a modest impact on performance, offering a solution to build secure and scalable PoS blockchains.
In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting.
The rapid development of the Internet of Things (IoT) has intensified security and privacy challenges across data generation, transmission, and storage. This study introduces a blockchain-based secure edge storage model tailored for IoT environments and presents a lightweight consensus algorithm, Deep Q-Network (DQN)-Raft+, which incorporates deep reinforcement learning. By combining the decentralized features of edge computing and blockchain, the model enables automated data access control through smart contracts. Furthermore, it optimizes leader node selection in the Raft consensus process using a DQN, formulating the consensus as a Markov Decision Process to enhance responsiveness and privacy protection in dynamic network conditions. Experiments were performed in a simulated environment using TensorFlow 2.6 and a MySQL database. The performance of DQN-Raft+ was compared against traditional consensus algorithms, including Proof of Work, Proof of Stake, Practical Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. Results indicate that DQN-Raft+ significantly reduces block generation delay (175.77 ms) and achieves a high privacy protection score (0.95). It also maintains a low data loss rate of 0.01%, demonstrating enhanced robustness and real-time capability. These findings indicate that DQN-Raft+ effectively strengthens data security and privacy in IoT systems, offering a technically sound and efficient mechanism for secure data exchange. The study provides both a theoretical framework and practical direction for future research in secure IoT deployment.
Asst. Prof. Panchami M Hegde, Asst. Prof. Swetha M
Carpooling has emerged as one of the most practical strategies for reducing the growing challenges of traffic congestion, fuel consumption, and environmental pollution, yet conventional carpooling systems that are operated through centralized platforms continue to face numerous issues that restrict their effectiveness and adoption. Existing solutions largely depend on intermediaries to coordinate between drivers and passengers, creating a system that lacks transparency, suffers from high service costs, and exposes user data to privacy risks and security breaches. Moreover, traditional systems are often criticized for inefficient dispute resolution, a reliance on single points of failure such as central servers, and the absence of mechanisms that foster accountability and long-term trust among users. These weaknesses make centralized carpooling platforms vulnerable to manipulation, biased practices, and technical outages, thereby limiting their scope as sustainable mobility solutions. To address these persistent challenges, blockchain technology—specifically the Ethereum ecosystem—offers a transformative alternative. Ethereum supports the development of decentralized applications (dApps) driven by smart contracts, which are self-executing agreements coded directly onto the blockchain. By embedding business logic into these contracts, processes such as ride creation, ride booking, payment settlements, user verification, and rating are automated, ensuring that interactions remain tamper-proof, transparent, and immune to third-party manipulation.
The accelerating adoption of electric vehicles (EVs) has revealed a significant challenge: ensuring accessible, secure charging infrastructure in areas with limited internet connectivity. This study introduces EVMCSDLT, a novel payment framework that leverages Distributed Ledger Technology (DLT) to facilitate secure transactions between EV users and mobile charging stations in offline environments. Unlike conventional payment systems that require continuous Internet connectivity, EVMCSDLT employs a two-part blockchain security mechanism using QR code authentication and hashing techniques. This mechanism distributes security data across both the sender's & receiver's devices, enabling transactions to be validated & recorded locally before subsequent synchronization with the blockchain network. The system is implemented using React.js integrated with Web3, supporting both online & offline transaction processing via MetaMask wallet. It also features real time geospatial tracking of fixed and mobile charging stations through Google Maps, allowing users to locate nearby charging options efficiently. Experimental results demonstrate a reliable transaction range of up to 8.13 meters between devices, consistent QR code scanning with an average response time of 3.4 s under various lighting conditions, and strong resistance to cyber threats in simulated man-in-the-middle attacks. The EVMCSDLT framework marks a significant advancement in extending the accessibility of EV charging technology to underserved areas while ensuring transaction security and integrity regardless of the Internet connectivity status.
Non-fungible tokens (NFTs) have emerged as a transformative innovation in art and technology, relying heavily on social networks for promotion and revenue generation. The value of NFTs is profoundly influenced by their scarcity, rarity, and unique breeding mechanisms, which present novel challenges for viral marketing strategies. In this paper, we introduce a new research problem of NFT Revenue Maximization (NRM), which focuses on maximizing revenue from the perspective of NFT marketplaces by optimally selecting users for viral marketing campaigns (NFT airdrops) and determining the ideal quantities of NFTs to release. We prove the hardness of NRM and propose an approximation algorithm named Quantity and Offspring-Oriented Airdrops (QOOA). Our algorithm leverages the concepts of Scarcity-Conscious Revenue and Valuation-based Quantity Inequality to prune suboptimal airdrops and quantities at an early stage. To further enhance revenue through NFT breeding, QOOA identifies and incentivizes Rare Trait Collectors to acquire multiple NFTs with rare traits, facilitating the breeding of high-value offspring. Experimental results demonstrate that QOOA significantly outperforms baselines, achieving up to 3.8 times higher revenue in large-scale social networks.
Engin Zeydan, Josep Mangues‐Bafalluy, Şuayb S. Arslan, Yekta Türk · 5 authors
The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology offers a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This article provides a comprehensive classification and categorization of blockchain-based network–sharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmos) is assessed for selected scenarios through extensive evaluations. This article also identifies gaps and opportunities in blockchain–based network sharing solutions and outlines future research directions.
The conventional space title industry is overwhelmed by centralized recorders that force tall recharging expenses, limit possession rights, and show security vulnerabilities due to single focuses of disappointment.This paper presents Domyn, a decentralized space title commercial center leveraging blockchain innovation and NFTs (Non-Fungible Tokens) to empower genuine possession of advanced spaces.Built on the Ethereum blockchain, Domyn utilizes ERC-721 keen contracts to tokenize space names, permitting clients to mint, purchase, offer, and exchange proprietorship without mediators.Furthermore, IPFS (InterPlanetary Record Framework) guarantees decentralized capacity, making the framework censorship-resistant and tamper-proof [8].Through an in-depth investigation of its design, exchange stream, and savvy contract execution, we illustrate Domyn's capacity to supply security, straightforwardness, and productivity compared to conventional space enlistment centers.The paper assist presents execution measurements, test case comes about, and a security assessment to approve the viability of the proposed framework.We moreover investigate future upgrades, counting Layer 2 scaling arrangements and multi-chain interoperability, to progress gas proficiency and selection.
Patrick Spiesberger, Jan Droll, Hannes Hartenstein
In the Ethereum system, the exclusion of specific transactions is currently feasible with minimal effort due to a power imbalance among entities. This censorship opportunity threatens the dependability of time-sensitive services deployable on Ethereum. In this paper, we look at this threat from an access control perspective and attribute it to a lack of accountability for censorship, a lack of policy definition and enforcement, as well as to the lack of disincentivization of policy violation. We propose an approach to enforceable policies in Ethereum. Furthermore, we demonstrate how a specific policy can address the shortcomings of existing censorship mitigation techniques, particularly Inclusion Lists. Under the assumption that block assemblers are unwilling to incur significant financial penalties as well as that the local view on the network messages is sufficiently consistent, the proposed approach guarantees the inclusion of a transaction in a block within 27 seconds in a non-saturated network. The empirical validation of sufficiently consistent views on outstanding transactions is currently in progress.
Juan Cano-Benito, Andrea Cimmino, Sven Hertling, Heiko Paulheim · 5 authors
Data spaces are emerging as decentralised infrastructures that enable sovereign, secure, and trustworthy data exchange among multiple participants. To achieve semantic interoperability within these environments, the use of semantic web technologies and knowledge graphs has been proposed. Although distributed ledger technologies (DLT) fit as the underlying infrastructure for data spaces, there remains a significant gap in terms of the efficient storage of semantic data on these platforms. This paper presents a systematic evaluation of semantic data storage across different types of DLT (public, private, and hybrid), using a real-world knowledge graph as an experimental basis. The study compares performance, storage efficiency, resource consumption, and the capabilities to update and query semantic data. The results show that private DLTs are the most efficient for storing and managing semantic content, while hybrid DLTs offer a balanced trade-off between public auditability and operational efficiency. This research leads to a discussion on the selection of the most appropriate DLT infrastructure based on the data sovereignty requirements of decentralised data ecosystems.
Cryptocurrency-related crimes are on the rise and have a wide-ranging impact across various areas. To effectively combat and prevent such crimes, cryptocurrency forensics, which relies on blockchain analysis, is essential. Despite advancements in Bitcoin de-anonymization techniques, several challenges persist. The absence of authentic data labels introduces uncertainty in de-anonymization results, especially in the context of address clustering. This issue is further compounded by the development of privacy-enhancing technologies that obscure address linkages, thus undermining the reliability of outcomes as forensic evidence. To address these limitations, this study focuses on Bitcoin blockchain analysis and the improvement of address clustering. Specifically, the work presents an enhanced simulation model designed to accurately simulate real Bitcoin transactions, offering a stable platform for evaluating address clustering algorithms that utilize transaction details, thereby facilitating the assessment of the admissibility of clustering results. Meanwhile, we introduce a new heuristic algorithm aimed at identifying one-time change addresses, with experimental results demonstrating that it achieves more precise clustering outcomes than existing heuristic methods. Furthermore, our blockchain analysis reveals overarching patterns and recent changes in the Bitcoin blockchain, particularly following the introduction of the BRC-20 token.
Abstract The breaches of the blockchain wallet keys greatly harm the security of blockchain transactions. To protect the secret keys, the known solutions, such as hierarchical deterministic wallets proposed in BIP32 or stealth addresses adopted in Monero, have been extensively researched. However, most of the existing works assume the key is safe, in the sense that it cannot be stolen or damaged, which is not true in practice. Moreover, current key revocation mechanisms either rely on centralized authorities, compromising decentralization, or require economic incentives to ensure nodes remain consistantly online. In this paper, we introduce Cocoon, the first blockchain wallet scheme that supports stealth addresses and provides a wallet revocation mechanism without the need for certificates. Cocoon not only ensures the privacy of wallet secret keys but also can individually revoke compromised keys with high performance. Our contributions are three-fold: First, we present the formal model and the related security definitions. Next, we give a generic construction based on the hierarchical identity-based signature, identity-based key encapsulation mechanism and non-interactive zero-knowledge proof. We then extend the scheme to the hierarchical setting for diverse scenarios. Finally, we give the implementation, and the results show that the scheme is practical.
The emergence of decentralized file-sharing platforms has introduced new challenges in ensuring secure data transmission, preserving node reputation, and preventing unauthorized access. This study presents an advanced peerto-peer (P2P) file-sharing system that integrates encrypted communication channels with blockchain-based consensus mechanisms to strengthen data confidentiality and trust evaluation. By employing protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS), the proposed architecture establishes a tamper-resistant and verifiable ledger of interactions. Cryptographic techniques are used to protect data in transit and maintain privacy-preserving trust scores without revealing user identities. The system's reputation module continuously aggregates behavioural metrics, adjusting trust values through dynamic weighting and anomaly detection. Experimental evaluation demonstrates that this approach achieves high resilience against Sybil and eavesdropping attacks while preserving the integrity and confidentiality of shared content. The proposed model offers a scalable and secure foundation for next-generation decentralized file-sharing systems.
The paper presents a novel approach to enhancing the crypto-economic resilience of decentralized networks by employing a mechanism of Random Time Challenge Tokens (RTCT). Contemporary consensus mechanisms (Proof-of-Work, Proof-of-Stake, Proof-of-Burn) and their ability to deter 51% attacks and Sybil attacks are analyzed. It is shown that classical approaches secure the network by imposing substantial economic barriers to attackers – for example, miners are forced to invest in hardware and electricity, raising the cost of a 51% attack to a prohibitively high level. However, vulnerabilities remain: attackers can rent computational resources or exploit under-active validators. The proposed RTCT mechanism involves generating random cryptographic challenges at unpredictable time intervals, which network nodes must answer with a cryptographically verifiable token, followed by burning of that token. This process creates unpredictable load and continuous costs for network participants, significantly increasing the economic cost of attacks. The RTCT process is mathematically formalized and the dependence of attack cost on the challenge complexity parameter m is evaluated. The results confirm that as complexity m increases, an attack requires exponentially higher expenditures, making the network more resilient. The advantages and potential drawbacks of the RTCT approach are discussed, as well as possible directions for further research, including optimizing challenge frequency and integrating RTCT with existing consensus protocols.
Abstract: The rapid proliferation of blockchain technology has intensified concerns over the energy inefficiency of traditional consensus mechanisms, particularly Proof of Work (PoW). This study investigates alternative, scalable consensus mechanisms with a focus on enhancing energy efficiency while maintaining performance and decentralization. The evaluation centers on Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Practical Byzantine Fault Tolerance (PBFT), using empirical simulations and statistical modeling to assess key performance indicators—energy consumption per transaction, transaction latency, and throughput. The results indicate that PoS is the most energy-efficient, consuming only 0.04 kWh per transaction, whereas DPoS offers the highest throughput at 1400 transactions per second with moderate energy requirements. PBFT demonstrates the lowest latency but at the cost of increased energy usage. Predictive regression analysis further reinforces the trade-offs between energy use and scalability across consensus mechanisms. These findings highlight the importance of selecting consensus algorithms based on specific application requirements and environmental considerations. The study provides a data-driven framework for guiding the design and adoption of energy-conscious blockchain infrastructures suitable for sustainable and large-scale deployment. Keywords: Blockchain, Consensus Mechanism, Energy Efficiency, Scalability, Proof of Stake, Delegated Proof of Stake, PBFT, Distributed Systems, Transaction Throughput, Green Blockchain Technologies