The MegaETH blockchain introduces new twists into improving healthcare transactions in efficiency and safety. MegaETH follows the hybrid consensus approach of PoS with BFT for solving some of the big issues in healthcare data management. Its strong encryption and zero-knowledge proof further enable significantly better protection of sensitive patient data, while reducing the risk of data breaches. It manages healthcare transactions fast and reliably, with a remarkable transaction throughput of about 10,000 transactions per second and a block duration of about one minute. Another important virtue of MegaETH architecture is that it uses less energy compared to more conventional Proof of Work systems. The demands of healthcare data are effectively managed with the scalability of the platform, underpinned by layer-2 solutions and sharding. MegaETH also illustrates excellent interoperability, as it will integrate with the existing systems of an institution and strictly abide by the rule of law. Moreover, smart contract executions are rather cheap, which enhances fraud prevention and accelerates administrative processes. The impacts from the adoption of MegaETH will be huge on reducing costs, ensuring data integrity, and finally improving patient care. Among the different options for solving current and future issues in health transaction administration, MegaETH is one of a kind.
In the context of rapid global transformations driven by the digitalization of the economy, politics, and the social sphere, rethinking approaches to constitutional and legal regulation becomes especially relevant. This article examines the challenges faced by modern constitutions due to the spread of digital technologies and analyzes emerging legal gaps in the regulation of new social relations. The author explores the theoretical foundations of the need to adapt constitutional norms to the conditions of digital reality, including virtual spaces, artificial intelligence technologies, distributed ledgers, and other elements of the digital environment. Special attention is given to the correlation between digital innovation and the foundations of constitutional order, the principles of the rule of law, and the protection of human rights under new conditions. In this context, the role of lobbying is analyzed as a mechanism for the legitimate representation of the interests of digital actors – technology corporations, civil society, and the expert community – in the process of legal transformation. Based on an analysis of legal literature and constitutional texts from various countries, the article concludes that targeted amendments to provisions concerning fundamental rights and freedoms, constitutional order, and the organization of public authority are inevitable. The article proposes a range of practical solutions, including the development of conceptual approaches to the legal recognition of digital rights, mechanisms for their implementation, and the institutionalization of lobbying activities as a tool for shaping a sustainable digital legal agenda. Thus, in modern conditions, lobbying acquires new significance as an element of constitutional architecture, reflecting the need for dialogue between the state and digital society—an element that requires constructive analysis and doctrinal study.
Decentralized authentication in dynamic mobile networks faces significant challenges due to high node mobility, resource constraints, and vulnerabilities to side-channel attacks. In this work, we present MobiAuth , a blockchain-driven framework based on Hyperledger Iroha and OMNET ++ that enables secure, peer-to-peer authentication using compact Ed25519 signatures and ephemeral session keys. Our protocol eliminates single points of failure by distributing trust across a permissioned ledger and employs constant-time cryptographic operations to thwart timing and power-analysis attacks. We validate MobiAuth through co-simulation in OMNET ++ integrated with Iroha via a Python gRPC bridge and benchmark its performance with Hyperledger Caliper. Simulation yields 95% packet delivery with an authentication latency ranging from 12 ms in the only OMNeT ++ and baseline to 20–150ms in the full ledger-integrated system, and a ledger write throughput of 250tps. Comparative experiments demonstrate a 33% reduction in communication overhead and robust operation under random Control Point failures and Byzantine Access Node behavior. Analysis of on-device ledger synchronization further highlights practical storage growth and bandwidth requirements for long-term deployment. These results indicate that MobiAuth achieves strong security and privacy with modest energy impact, scalable performance, and compatibility with mobile devices in real-world network environments. • Vulnerabilities of mobile network devices in a dynamic environment. • Blockchain-based automatic authentication for mobile devices. • Enhanced security and privacy with Ed25519 curve cryptography. • OMNET++ simulation on Hyperledger Iroha for mobile network. • Protocol verification using Scyther for testing security protocol strength.
Widespread adoption of Blockchain Technology has emphasized smart contracts as vital units of digital transactions. In the brief history of smart contracts, significant losses have occurred owing to unexplained vulnerabilities in blockchain-loaded contracts. Smart contract vulnerabilities endanger blockchain technology's viability and confidence. Blockchain-based applications rely on smart contracts to automate and trustlessly execute agreements. Their immutable and self-executing nature leaves them vulnerable to security vulnerabilities, which have caused major financial losses in decentralized platforms. Traditional static and symbolic analysis tools often miss sophisticated or obfuscated vulnerabilities, resulting in insufficient coverage or excessive false positives. These restrictions led the investigation of Deep Learning (DL) algorithms for detecting vulnerabilities in smart contract crucial since they can learn complicated patterns from code representations without manual feature engineering. Deep learning based methods for detecting vulnerabilities in smart contracts are thoroughly evaluated. We rigorously assess state-of-the-art DL models including BiLSTM, BiGRU, CNNLSTM, GCN, and CodeBERT using publicly available dataset. We provide a taxonomy of DL-based detection techniques and standardize performance criteria including accuracy, F1-score, and detection latency. Experimental results show model architecture trade-offs in detection, computational efficiency, and generalization.
Abstract Purpose The purpose of this study is to adapt a Bayesian dual‐virtual non‐contrast (VNC) method by integrating prior anatomical knowledge from AI‐based multi‐organ segmentation and to generalize it for spectral photon‐counting CT (PCCT) with an arbitrary number of energy channels. Methods A previously proposed Bayesian VNC method is reformulated for any number of energies and adapted for integration with AI segmentation. TotalSegmentator, an open‐access whole‐body AI segmentation model, is used to provide spatial priors. The method is applied to simulated contrast‐enhanced dual‐energy CT (DECT) and PCCT datasets from eight virtual patients, with and without AI segmentation. Key radiotherapy‐relevant parameters such as electron density () and proton stopping power ratio (SPR) are estimated and compared to ground truth values. Additional results are obtained for non‐contrast scans by setting contrast agent uptake to zero. Results AI‐based segmentation improved the accuracy of parameter estimation for both DECT and PCCT, with a more pronounced effect for PCCT. The combination of high spectral resolution and anatomical priors led to reduced RMS errors in SPR and . Mean absolute water‐equivalent path length (WEPL) errors confirmed the superiority of segmentation‐assisted PCCT over other methods. Conclusion This proof of concept demonstrates a flexible, AI‐assisted Bayesian framework for extracting quantitative information from contrast‐enhanced spectral CT. By integrating AI segmentation and generalizing to PCCT, the method shows improved tissue characterization, suggesting the value of AI in extracting quantitative information beyond DECT. Further validation on clinical datasets is needed. Background Quantitative VNC methods offer the potential to extract radiotherapy‐related parameters from contrast‐enhanced spectral CT without the need for additional non‐contrast imaging. However, the inherently ill‐posed nature of tissue characterization from limited spectral data remains a major limitation, which requires advanced techniques.
Blockchain has emerged as a promising technology for enabling decentralized, tamper-evident, and auditable data sharing among multiple untrusted parties. However, practical deployments in distributed computing environments face a persistent trade-off between scalability and privacy. Public blockchain networks often expose transactional metadata, compromising confidentiality, while privacy-preserving blockchains—such as those leveraging zero- knowledge proofs (ZKPs)—typically suffer from reduced throughput and increased latency due to the computational overhead of proof generation and verification. Similarly, scalability-enhancing techniques like Layer- 2 rollups, sharding, and state channels often provide minimal privacy guarantees, leaving sensitive metadata vulnerable to inference attacks. This paper proposes a privacy-preserving and scalable blockchain architecture designed specifically for secure data sharing in distributed systems, such as federated cloud platforms, healthcare data networks, IoT ecosystems, and inter-bank settlements. The architecture integrates Layer-2 zero- knowledge rollups with a modular Layer-1 settlement layer (Ethereum or Hyperledger Fabric), decentralized storage (IPFS/Filecoin), and fine-grained access control mechanisms. By batching transactions off-chain, generating succinct ZK proofs for validity, and committing only aggregate proofs and state roots to the base chain, the system achieves both confidentiality and high throughput. The architecture is deployed in a Kubernetes-orchestrated environment, enabling horizontal scaling, automated failover, and comprehensive observability through Prometheus, Grafana, and Jaeger. A prototype implementation demonstrates a throughput improvement of up to$5.8 \times$over baseline privacypreserving blockchains, with latency remaining within acceptable limits for distributed applications. Our evaluation framework compares the proposed design against three baselines— Layer-1 only, Layer-1 + privacy, and Layer- 1 + scalability—and includes metrics such as throughput, latency, cost, privacy efficacy, and fault tolerance. The results indicate that combining privacy-preserving Cryptography with scalable rollup architectures is both feasible and beneficial for real-world distributed systems, offering a compelling pathway toward secure, high-performance blockchain applications.
In 2023, over 15 million health records were exposed, highlighting vulnerabilities in traditional Electronic Health Records (EHRs). This paper proposes a novel Ethereum-based decentralized EHR system to enhance security, privacy, and patient agency in Healthcare 4.0. Unlike prior systems like MedRec and Patientory, our framework introduces a time-bound emergency access protocol, reducing critical care response time by$\mathbf{2 0 \%}$, and an AI-driven module for digitizing paper records. ZeroKnowledge Proofs (ZKPs) ensure privacy-preserving verification, while the InterPlanetary File System (IPFS) provides scalable, tamper-proof storage. A ReactJS application with MetaMask empowers patients with data ownership. Integrated with FHIR/HL7 standards and GDPR-compliant, the system supports smart city initiatives. Prototype testing demonstrates robust scalability, paving the way for patient-centric national digital health frameworks.
Non-Fungible Tokens (NFTs) have gained attention as a technology for guaranteeing ownership of digital content, leading to rapid market expansion. However, NFTs are limited in that they guarantee ownership only for a single, explicitly designated digital asset. For instance, if an image associated with an NFT undergoes modifications such as resolution reduction or trimming, it falls outside the scope of the NFT’s guarantee. In this study, we propose a new NFT scheme capable of guaranteeing ownership for multiple digital assets that fall within a defined visual similarity threshold. The core of this method lies in replacing conventional cryptographic hash functions with Image Hash functions, allowing the scope of ownership to cover a "range" of similar content rather than a single exact match. This enables highly similar content to be automatically included within the NFT’s scope of guarantee without explicit designation. To verify the feasibility of this scheme, we implemented and evaluated a prototype using four types of Image Hash functions against common image transformations, such as resolution reduction and trimming, on the Polygon blockchain. The results indicate that both Average Hash (aHash) and Perceptual Hash (pHash) are suitable functions, and that the NFT verification process can be performed efficiently. This method provides a novel mechanism to dynamically extend the scope of NFT ownership, paving the way for new potential applications for NFTs.
Advanced Steganography and Watermarking Techniques
Blockchain technology, particularly Ethereum, has enabled a wide range of decentralized applications but continues to face significant scalability challenges, including high gas fees, limited transaction throughput, and frequent network congestion. This review provides a comprehensive analysis of Ethereum's scalability constraints and critically evaluates current Layer 2 scaling solutions such as Rollups, Plasma, the Lightning Network, and other emerging alternatives. The study conducts a comparative assessment of these mechanisms with respect to decentralization, privacy, and security-core dimensions of the blockchain scalability trilemma. Recent academic contributions, including stochastic models of gas fee behavior and protocolspecific analyses, are examined to provide both theoretical and empirical insights. Emphasis is placed on understanding the architectural advantages of zk-Rollups and their positioning as a leading Layer 2 solution. Quantitative metrics such as transaction throughput benchmarks, privacy, and security capability comparisons are presented to support the evaluation. The findings are intended to inform the design of more scalable, secure, and privacy-preserving Ethereum-compatible architectures.
Anders Malta Jakobsen, Oliver Holmgaard, Daniele Dell’Aglio, Michele Albano
As Ethereum is one of the most popular blockchains, it is naturally targeted by various attacks, aiming, for example, to disrupt the service or steal tokens. Among these, in deanonymization attacks, an adversary can obtain validator IP addresses and then perform a Denial-of-Service attack on them. To mitigate this attack, the Ethereum foundation is proposing Whisk, a Single Secret Leader Election protocol that uses a zero-knowledge proof called Curdleproofs to prove the validity of a shuffle of validators. One limitation of Curdleproofs is the shuffle size, which must be a power of two, restricting the number of validators that can be included. This paper overcomes this limitation by proposing CAAUrdleproofs, a modified version of Curdleproofs that incorporates Springproofs. Our experiments show that CAAUrdleproofs offers a performance advantage for any shuffle size that is not a power of two and that this advantage increases as the shuffle size decreases below a power of two.
This paper presents a decentralized authorization framework that employs non-transferable Non-Fungible Tokens (NFTs), also known as soul-bound tokens (SBTs), to deliver tamper-resistant, identity-bound access control. Access rights are permanently linked to a user's blockchain wallet and can be upgraded via metadata updates without reminting, enabling dynamic tier management while preventing resale or delegation. The architecture combines an ERC-721 smart contract backend with a decentralized front-end interface for wallet-based authentication and administrator-controlled content distribution. Implementation on the Binance Smart Chain test network demonstrates secure and cost-effective access management with tiered content control. An experimental evaluation quantifies the gas costs associated with NFT minting, tier upgrades, and InterPlanetary File System (IPFS) metadata storage. Results show that using the native Base32 encoding of IPFS content identifiers reduces storage gas costs by approximately 6% compared to the previously standard Bitcoin-style Base58 encoding. Furthermore, shorter uniform resource identifiers (URIs) avoid costly expansions of the storage slots of the Ethereum Virtual Machine (EVM), which can increase transaction costs by 20,000 gas per slot. These findings provide practical guidelines for designing scalable and cost-effective NFT-based authorization systems. Source code and implementation details are available on GitHub.
Zeinab Alipanahloo, Michael Duchesne, Kaiwen Zhang
RzkFL is an end-to-end, privacy-preserving machine-learning framework that fuses Federated Learning (FL) with recursive zero-knowledge proofs (ZKPs) to protect data, models, and users while unlocking verifiable inference. Models are trained entirely on local devices, so sensitive data never leave the premises. The resulting model can be monetized by offering verifiable predictions on a pay-per-use basis. During inference, each customer independently computes predictions using private data, making it essential to verify that these inference results are computed correctly and honestly. Unlike existing approaches that rely on heavy communication or centralized trust assumptions, RzkFL allows each customer to generate a cryptographic proof of correct local inference, which can be succinctly verified without revealing input data or model parameters either by the customer or a third party. The core innovation lies in the use of recursive ZKPs, enabling each customer to generate small, composable proofs for intermediate layers of neural network inference. These proofs are then recursively aggregated into a single succinct proof using the Nova proof folding scheme. Nova’s design eliminates the traditional sequential dependency of recursive proofs by enabling incrementally verifiable computation through a folding scheme. RzkFL supports on-chain verification via Ethereum smart contracts, allowing AI results to flow directly into financial workflows. A decentralized file storage system maintains the integrity and availability of the global model. We introduce specialized circuits for input, hidden, and output layers to optimize proof generation time and gas costs. The customer can generate proof for the entire inference computation or delegate the proof generation for the intermediate layers and the output layer to another party. The design suits privacy-preserving machine learning scenarios where customer devices are resource-constrained. Our results show that RzkFL can significantly reduce proof size and verification costs while maintaining privacy, integrity, and scalability in federated inference. This makes it a compelling approach for real-world decentralized AI systems requiring strong verifiability guarantees.
Blockchain technology offers incredible value and opportunity in delivering secured, decentralized transactions but faces significant challenges as it relates to scalability, which hinders adoption and use. As in the maturation of blockchain technology, challenges related to its scalability are important for latest technologies as they can affect scalability and efficiency. These types of challenges to scalability generally emerge through increased user and transaction volumes and cause losses in performance and efficiency as they pertain to a congested network, storage of data, and speed in processing time. Additionally, traditional consensus mechanisms, like proof of work, seamlessly create challenges as they pertain to amounts of computational power, and other resources allow allocation associated with work completion. As we can imagine, potential solutions have already emerged that help to mitigate overcoming these challenges. Solutions related to Layer 2 scaling, such as side chains or payment channels, add an alternative layer to the transaction applications and, theoretically, increase throughput and financially through decreased network congestion. Sharding, which splits the blockchain into smaller, more manageable segments, also improves operational efficiency. Moreover, advancements in consensus algorithms, including Proof of Stake and hybrid models, aim to boost scalability while optimizing resource use. This research explores the primary scalability challenges faced by blockchain systems. It reviews the cutting-edge solutions being developed to improve their performance, ensuring that blockchain technology can effectively support the increasing demands of a decentralized digital world.
MegaETH leads the way in blockchain technology. It created the Real-Time Proof of Stake (RTPoS) consensus method. This new approach tackles regular blockchain networks' main speed and scaling issues in regular blockchain networks. It allows fast transaction processing without giving up security or decentralization. Meg+aETH focuses on high output and can handle thousands of transactions per second (TPS). This opens doors for many decentralized apps (dApps) across different fields. A big plus of the Ethereum platform is how MegaETH fits into the Ethereum ecosystem. It uses smart contract features and works well with the Ethereum Virtual Machine (EVM). This compatibility helps the ecosystem grow and brings new ideas by making it easier for more developers to join in. MegaETH also cares about the environment. Its design uses less energy, meaning it has less impact on nature than proof-of-work systems. MegaETH brings together efficiency, security, and the ability to grow. This sets a new bar for real-time blockchain apps. As a result, it speeds up how the economy takes on decentralized solutions. It also lets developers and companies explore new ways to use this technique.
Ethereum is currently the main blockchain ecosystem providing decentralised trust guarantees for applications ranging from finance to e-government. A common criticism of blockchain networks has been their energy consumption and operational costs. The switch from Proof-of-Work (PoW) protocol to Proof-of-Stake (PoS) protocol has significantly reduced this issue, though concerns remain, especially with network expansions via additional layers. The ERC-4337 standard is a recent proposal that facilitates end-user access to Ethereum-backed applications. It introduces a middleware called a bundler, operated as a third-party service, where part of its operational cost is represented by its power consumption. While bundlers have served over 500 million requests in the past two years, fewer than 15 official bundler providers exist, compared to over 100 regular Ethereum access providers. In this paper, we provide a first look at the active power consumption overhead that a bundler would add to an Ethereum access service. Using SmartWatts, a monitoring system leveraging Running Average Power Limit (RAPL) hardware interfaces, we empirically determine correlations between the bundler workload and its active power consumption.
S. Parvathi, I Umamaheshwar Rao, Kummari Venkatesh, Yacharam Uma · 6 authors
Information is crucial in every area of activity in the contemporary digital era. Safe processing and storage of data are required, especially for software handling sensitive information. Because electronic data can be manipulated, it must be protected from unauthorized alterations. Organizations are always exposed to cyberattacks where malicious actors attempt to alter crucial information. Because preservation of the digital evidence as original and its source is still very important in an investigation, such an event is very alarming. Because every data breach case has a likely impact on the legal process, evidence preservation and maintaining watch at every point of existence becomes very vital. The custody of evidence relies on reports passing through several middlemen, i.e., pathology labs, physicians, and police. One healthy method of obtaining security, immutability, and transparency in the process is with the help of blockchain technology. Blockchain makes the transfer of evidence possible and traceable securely without relying on a single central entity by decentralizing data management. In this paper, we suggest an Ethereum blockchain-based system for evidence management with the help of Ethereum. Through use of IPFS in decentralized storage, the system optimizes security through secure storage of evidence and reports with simplicity of access. Additionally, interoperability between agencies without sacrificing security levels guarantees smooth transmission of data. The proposed solution effectively protects against tampering and guarantees evidence as credible in the course of the investigation process due to the high integrity, traceability, and immutability capabilities of blockchain.
Academic certificate verification is plagued by on-going issues of fraud, manipulation, and inefficiency in the digital age, which erodes credibility and trust among people and organizations. Using the Ethereum blockchain's fundamental features of immutability, decentralization, and fraud prevention, this study explores how to safely issue and validate academic cre-dentials. By connecting certificates to tamper-proof cryptographic hashes stored on the blockchain, the suggested system uses smart contracts to automate certificate issuance and validation. The framework greatly reduces credential falsification and simplifies verification for employers and educational institutions by doing away with reliance on centralized authorities and offering transparent, auditable records that are available to authorized stakeholders. Significant gains in security, processing speed, and stakeholder confidence are shown when compared to conventional verification techniques. Significant gains in security, processing speed, and stakeholder confidence are shown when compared to conventional verification techniques. As a strong way to improve trust, integrity, and operational efficiency in international cer-tificate verification procedures, the findings support the use of blockchain technology in academic credential management.