Non-fungible tokens (NFTs) and other Web3 multimedia are typically stored off-chain because image and video assets exceed practical on-chain storage budgets, creating a gap between immutable ownership records and long-term media availability. This paper proposes a collection-level neural compression approach that converts an entire NFT collection into a single compact decoder. The decoder maps a token's integer index directly to its reconstructed image, and is intentionally trained to memorize the collection so that no per-image latent codes are stored. To minimize the decoder's on-chain footprint, we parameterize weights in the frequency domain, progressively prune high-frequency coefficients via zigzag-ordered masking, and apply run-length plus Huffman entropy coding to the resulting sparse parameters. Across three 10,000-image NFT benchmarks, Bored Ape Yacht Club (BAYC), Azuki, and CryptoPunks, the compressed artifacts are reduced to 7.63 MB, 17.09 MB, and 3.33 MB, respectively. These artifacts achieve up to \(177\times\) smaller size than PNG while maintaining high reconstruction quality, measured by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), with PSNR \(\geq\) 33 dB and SSIM \(\geq\) 0.967. We further report Ethereum storage gas estimates showing that collection-level on-chain deployment becomes feasible at \(10^{3}\) – \(10^{4}\) USD under representative fee assumptions, reducing on-chain deployment cost by 58.6%–99.4%. The main contribution is a collection-level compressed decoder that serves as the deployable on-chain storage artifact, removing dependence on external media hosts and avoiding per-image latent storage. These results demonstrate a practical path to self-contained, on-chain availability of large NFT multimedia collections without relying on external storage networks.
Advanced Data Compression Techniques
Advanced Steganography and Watermarking Techniques
We present an empirical performance evaluation of SILM, a national-scale music-royalty administration platform prototyped for LMKN, Indonesia's collective rights management agency, implemented as a chain of ten event-driven Go microservices connected through an in-memory publish/subscribe bus (Apache Kafka in the production blueprint). The study contributes a Dapper-style trace-per-event instrumentation yielding per-stage latency distributions, a five-point throughput sweep from 100 to 10,000 play events used to locate the operating point and the degradation knee, and a money-conservation and correctness suite. At every tested burst scale up to 10,000 events the pipeline delivers 100\% event delivery and exact money conservation, while median end-to-end (E2E) latency grows approximately 17-fold (0.81 s at 100 events to 13.7 s at 10,000 events); per-stage spans attribute 85.0\% of E2E average latency at the largest scale to a single cross-service queueing stage. A 30-second sustained-load soak at approximately 1,042 events/s exposes the single-consumer ceiling: the bounded subscriber queue overflows in its tail, dropping 7,097 of 31,255 submitted plays (22.7\%), the first measured reliability failure of the platform. All findings are compared against recent published results on tail latency, bottleneck attribution, and channel sizing in event-driven architectures.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
We present a comparative implementation of the same financial settlement circuit in five zero-knowledge proofsystems—Groth16, Halo2/IPA, STARK/FRI, PLONK/KZG, and Nova/folding—and report the design consequencesthat appear only when the comparison is performed on a complete stateful application rather than onmicrobenchmarks.The application is a minimal settlement layer with double-entry value conservation, spending authority, doublespendprevention, threshold issuance, burns, selective disclosure for supervision, and account freezing. Unlikebenchmark suites centered on primitives such as SHA-256, this setting forces persistent state updates, globalinvariants, and multi-authority rules into every backend.We document nine implementation findings absent from typical comparative tables. The most consequential forarithmetization is that AIR lacks native copy constraints, which creates a silent soundness gap when updatingMerkle state unless a lockstep dual-climb pattern is enforced. The most consequential for the application is thatsingle-step settlement discloses the recipient’s balance to the payer, which forced a two-phase transferdesign. We also report identical-condition measurements for setup, proving, verification, and proof size; explainwhy Groth16 was rejected despite superior proof size; and record methodology errors that were detected andcorrected during the work.The reference implementation is public and test-backed. It is not third-party audited and does not implementdistributed consensus. We treat both limitations as part of the result.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
In this chapter, you will learn how to write, deploy, and interact with smart contracts using Solidity. We will cover fundamental data types, control structures, functions, and contract organization. You will understand the Ethereum Virtual Machine, how contracts send and receive Ether, and how to use events, modifiers, and visibility specifiers. The chapter also introduces reference types like arrays and mappings, common security practices, and techniques for optimizing gas usage.
Gas metering on EVM-compatible blockchains assumes that execution conditions are stable: that the resource mix is constant enough to justify collapsing execution costs into a single scalar with fixed relative prices, and that state drift between submission and execution does not materially alter a transaction's outcome. We measure the extent to which this assumption fails. We present a trace-level measurement study of EVM workloads on Ethereum (L1) and Base (L2) throughout 2025, sampling 3,000 blocks per day per chain. We decompose each transaction into opcode-level execution gas, intrinsic gas, refunds, and persistent state deltas. To measure state sensitivity, we re-execute transactions from September 2025 on older states and record how gas usage and storage access patterns change. We find the resource mix to be far from stable: on Base, storage reads and compute account for 29.2% and 24.3% of execution gas, while Ethereum devotes 34.9% to storage writes. Ethereum's gas limit doubling during 2025 shifted its own profile toward compute-heavier, Base-like patterns. Base also exhibits a higher fraction of cold storage reads (49.7% versus 39.6% on Ethereum). Persistent state growth, a permanent cost priced as a transient one, reaches 456 GB on Base versus 38 GB on Ethereum. Execution outcomes are equally unstable: gas estimates vary across nearby historical states for 46.0% of transactions on Base, compared to 13.9% on Ethereum, with especially high sensitivity for MEV and DeFi activity. Storage access patterns also diverge across states, limiting the effectiveness of access lists and complicating parallel execution. Our work provides an empirical foundation for multi-dimensional gas metering and explicit pricing of state growth. They show that state-sensitive execution behavior complicates workload estimation, directly affecting transaction predictability and user experience.
The rapid proliferation of digital media necessitates resilient paradigms for managing, authenticating, and preserving static and dynamic 2D data. Since centralized repositories are vulnerable to tampering and pure blockchain storage remains economically prohibitive for high-fidelity multimedia, this comprehensive review demonstrates that a hybrid on-chain/off-chain architecture constitutes the most viable solution. By anchoring immutable metadata on robust ledgers while offloading heavy graphical payloads to distributed networks like IPFS and Arweave, this paradigm optimizes both security and cost. For static 2D formats, current research emphasizes cryptographic provenance, digital rights management, and tamper detection via perceptual hashing. Conversely, dynamic 2D formats require advanced architectural optimizations, including decentralized streaming protocols, progressive rendering, and complex temporal metadata indexing. Despite these technological advancements, widespread adoption is severely impeded by critical bottlenecks such as network scalability limits, fragmented cross-chain interoperability, and the absence of universal benchmarking datasets. To bridge the gap between experimental frameworks and enterprise integration, future research must prioritize developing interoperable metadata schemas, Layer-2 performance optimizations for high-bandwidth streaming and integrating privacy-preserving cryptographic primitives like Zero-Knowledge Proofs. Ultimately, this paper provides a foundational roadmap for architecting scalable, decentralized digital asset management ecosystems.
Zero-knowledge proof (ZKP) is a promising cryptographic protocol, but its practical deployment is hindered by the time-consuming proof generation. The proof generation inherently exhibits high-degree parallelism, yet challenges persist in exploiting fine-grained parallelism due to the dataflow complexity, impeding previous work to achieve optimal acceleration. In this work, we propose FZKP, a ZKP accelerator that utilizes two novel fine-grained dataflows coupled with two forward-flow microarchitectures to alleviate dataflow complexity, efficiently exploiting fine-grained parallelism. The proposed dataflows simplify the dataflow pattern for parallel execution, disclosing fine-grained parallelism at a low cost. The microarchitectures employ a base design to handle large bit-width intermediate results for timely consumption. They then replicate and combine the base design following the proposed dataflow to facilitate parallel execution. When evaluated in 12 nm, FZKP achieves an average speedup of 10.3× and 2.2× over the state-of-the-art GPU-based solution and ZKP accelerator on real-world workloads, respectively.
Alternative data availability (AltDA) systems provide Ethereum L2s with an external data publication layer for high throughput rollup designs. By moving bulk data publication outside of Ethereum, AltDA allows L2s to process more data than native DA. However, this replacement introduces a new consensus critical integration layer. Existing ecosystem frameworks identify high level risks, such as external DA trust assumptions and the presence or absence of a DA verifier, but do not provide a complete specification for how an L2 should integrate with AltDA. This gap can lead to L2 halts, inconsistent derivation across honest L2 nodes, invalid state assertions, or bridge attacks. This paper presents a canonical validation framework for secure AltDA integration. We model the boundary as a typed, deterministic, and total translation from L1 inbox bytes to an AltDA commitment, then to externally available data, and finally to the rollup payload consumed by the rest of core L2s logic. The central principle is that every adversarial input must lead to a defined unique outcome. We show how missing obligations lead to concrete failure modes, including underconstrained settlement, derivation halts, inconsistent honest node behavior, invalid state assertions, and bridge safety failures. We then apply the framework to representative AltDA integration architectures, including Celestia-Blobstream, EigenDA based designs, and Avail-ZKsync. Our evaluation shows that secure AltDA integration is not determined solely by the DA provider or bridge. The surrounding L2 integration must also enforce the full validation relation connecting L1 inbox inputs to accepted L2 state.
Decentralized storage offers high availability and scalability. However, owing to the decentralized storage of data across multiple nodes, issues such as slow data access and complex operations arise, resulting in a poorer user experience compared to centralized storage. To address this, a data availability sampling technology is employed, which maintains the decentralized nature of the method while incorporating the advantages of centralized storage. In data availability sampling technology, multiple nodes obtain a smaller, randomly selected subset of data from a single data owner. This technology is often combined with erasure coding to enhance data availability. Based on data availability sampling technology, decentralized storage providers are introduced to serve users on a one-to-one basis, and data guarantors supervise storage providers and provide guarantees for user data. A comprehensive storage method is designed to achieve highly available data storage, and blockchain and smart contracts are employed to enhance decentralization. By supporting a repledging model and adopting a storage-proof algorithm with low computational resource consumption, the willingness of the nodes to join is increased. To resolve the contradiction between large data scales and the limited bandwidth resources of data guarantors, a delayed confirmation mechanism is proposed. Experimental and analytical results show that under this method, the probability of malicious node collusion is only 2.43×10<sup>-3</sup>, the probability of untrustworthy data availability sampling results is only 2.93×10<sup>-4</sup>, the number of data unavailability occurrences is 0 in 3 million simulation experiments, the number of centralized nodes is 0, and generating storage proofs for a 1 MiB file takes only 3.51 ms. This method achieves highly available data storage while improving user-friendliness and node-friendliness, providing a feasible technical path for optimizing decentralized storage.
The rapid digitalization of global infrastructure has amplified the vulnerabilities inherent in centralized cloud storage systems, where single points of failure, administrative access abuses, and external cyberattacks routinely compromise sensitive data.Traditional cloud architectures rely on centralized trust models that frequently succumb to data breaches and censorship.To address these critical flaws, this paper proposes De-Drive, a zeroknowledge, hybrid decentralized storage application (DApp) that seamlessly integrates Web3 architecture with robust cryptographic protocols.De-Drive leverages a hybrid storage model: heavy file payloads are stored off-chain on the decentralized InterPlanetary File System (IPFS) via Pinata nodes, while the immutable reference links-Content Identifiers (CIDs)-are permanently logged on the Ethereum blockchain (Sepolia Testnet) utilizing a custom, highly gas-optimized Solidity smart contract.To solve the inherent privacy flaws of public IPFS networks, De-Drive implements strict client-side Advanced Encryption Standard (AES) cryptography.Files are converted to Base64 strings and encrypted locally within the React frontend utilizing a user-defined symmetric key prior to network transmission.This architecture ensures absolute zero-knowledge storage; neither the IPFS nodes hosting the data nor the blockchain observers auditing the ledger can decipher the underlying content without the specific decryption key.The proposed architecture successfully resolves the blockchain storage trilemma by delivering a decentralized, immutable, and strictly private data vault, demonstrating significant improvements in cost-efficiency and security over both traditional cloud services and purely on-chain storage alternatives.
Este artigo analisa estratégias de otimização de gas em Ethereum a partir de duas dimensões principais: o custo dos opcodes da EVM e as escolhas de estruturas de dados em Solidity. A tabela de opcodes da EVM e a evolução do gas schedule mostram que operações de armazenamento e acesso externo, como SSTORE, SLOAD, CALL, BALANCE e EXT*, estão entre as mais caras, especialmente após EIPs como a 2929, que aumentaram o custo de acessos “frios” a contas e slots de storage para refletir melhor seu impacto na execução e na camada de armazenamento. Estudos recentes sobre custos de armazenamento evidenciam que uma escrita em SSTORE pode custar cerca de 22.100 gas para 32 bytes (aprox. 690 gas/byte), enquanto leituras via SLOAD também são significativamente caras, motivando pesquisas sobre técnicas como SSTORE2 e mecanismos para corrigir “overcharge” em leitura/escrita de storage, com ganhos médios de até 30–32% em fees para certos padrões de uso. Boas práticas de otimização de gas em Solidity incluem reduzir o número de acessos a storage movendo valores frequentemente lidos para variáveis em memória, empacotar variáveis em slots de 32 bytes (storage packing), preferir tipos fixos a dinâmicos quando possível, evitar cópias desnecessárias de arrays de storage para memória e desenhar estruturas de dados que minimizem gravações em storage. A literatura e guias de otimização indicam que a escolha entre arrays, mappings, structs e padrões de layout impacta diretamente o custo de execução, especialmente em loops que interagem com storage ou estruturas dinâmicas. Conclui‑se que a otimização de gas em Ethereum é um problema tanto de engenharia de baixo nível, ligado ao custo de opcodes e ao modelo warm/cold de acessos, quanto de design de dados e algoritmos, com implicações econômicas diretas para usuários, protocolos DeFi e estratégias de design de L2s.<br>
We present HyperVerITAS, a new zero-knowledge proof (ZKP) system for image provenance that enables scalable, efficient, and privacy-preserving verification of image transformations. HyperVerITAS builds upon the same minimal trust model as VerITAS (IEEE S&P '25), requiring trust only in the image source device, while treating the editing software as untrusted. Unlike VerITAS, which relies on FFT-intensive SNARKs and suffers from high memory overhead (up to 120 GB), HyperVerITAS leverages multilinear polynomial encodings over the Boolean hypercube to dramatically reduce both proving time and memory usage. Our design cleanly separates signature verification from image transformation, supports modular integration of multiple polynomial commitment schemes (including post-quantum constructions) and naturally extends to a wide range of affine image transformations. We implement HyperVerITAS with two distinct commitment schemes (Brakedown and multilinear KZG) and evaluate it on full-system pipelines involving cropping and grayscaling. On commodity hardware (Apple M3, 36 GB RAM), HyperVerITAS generates proofs for 33 MP images using only 27 GB of RAM and 6.6 minutes of proving time, whereas VerITAS fails to scale beyond 4 MP. These results establish HyperVerITAS as a practical and scalable ZKP system for secure and efficient image provenance.
Highlights • Identifying threats that cannot be countered by theoretical security based on STRIDE threat analysis of an existing provable data possession (PDP) system • Designing a PDP system with practical security features to counter threats that cannot be addressed with theoretical security alone • Presenting the implementation of the proposed PDP system, PRISM, which is also provided as an open-source software • Validating security properties through property-based fuzz testing with 10,000 randomized test runs per security property • Demonstrating PRISM’s key strengths through comprehensive experiments, including basic performance, trade-offs between processing time and data auditing efficiency, and capabilities for detecting data anomalies Digital platforms are increasingly recognized as a cornerstone for advanced virtual spaces such as smart cities and the metaverse, where vast amounts of data are aggregated, analyzed, and utilized to make critical decisions. These platforms rely on data fusion to integrate diverse sources of information, encompassing individual behavior, urban dynamics, and system states. Through auditing against data tampering, loss, and substitution, enabling the detection of such threats is critical to building a highly reliable system. This paper introduces PRISM (Provable and Immutable Storage Mechanism), an Ethereum-based Provable Data Possession (PDP) system designed to integrate data reliability and security with decentralized auditing. PDP, a cryptographic protocol that enables data integrity in untrusted cloud storage, has seen extensive research focusing on theoretical security and computational efficiency. PRISM extends this foundation by addressing practical security concerns, including the integration of authentication and authorization, data immutability, data uniqueness, data freshness, and state management, to ensure a robust system implementation. Experiments on processing costs and parameter analysis reveal a trade-off between the costs and detection accuracy and demonstrate that PRISM provides efficient data auditing.
Yuqin Wang, Zhengkang Du, Jiannong Cao, Shan Jiang
The proliferation of web3 has underscored the need for robust and efficient decentralized data infrastructure. However, prominent decentralized storage systems like IPFS suffer from poor data availability and the risk of cold data loss, while incentive-based solutions like Filecoin and Storj employ uniform replication strategies that are often inefficient and costly. These systems fail to distinguish between frequently accessed ("hot") and infrequently accessed ("cold") data, leading to a suboptimal allocation of storage resources. This paper introduces TRep, a novel Temperature-aware Data Replication strategy designed to address these challenges. TRep implements a dynamic replication policy by classifying data based on its access temperature. The system is built on three key innovations: (1) a highly accurate data classification algorithm using double exponential smoothing to analyze access frequency and trends; (2) an intelligent data distribution strategy that selects optimal backup nodes based on storage availability, online stability, and network proximity; and (3) a dynamic backup regulation mechanism, managed by a regulator node, to globally control replica counts for hot and cold data. Our performance evaluation demonstrates that TRep's classification algorithm significantly reduces prediction errors compared to traditional methods. Furthermore, TRep achieves speeds up to four times faster than IPFS for large files while effectively managing storage overhead. TRep thus provides a flexible and performant framework for data replication, enhancing the durability and efficiency of web3 data infrastructure.
The exponential growth of digital data has intensified reliance on cloud storage, yet conventional centralized architectures remain persistently vulnerable to unauthorized access, data tampering, and privacy violations. This paper presents a novel blockchain-driven approach for decentralized cloud storage that addresses these concerns through a multi-layered security mechanism. The proposed system fragments a user’s file into multiple independent blocks, encrypts each block using the AES-256 algorithm with PBKDF2-derived keys, and distributes them across distinct nodes of the InterPlanetary File System (IPFS). The corresponding IPFS hash addresses are then recorded on an Ethereum-based blockchain through a Solidity smart contract, ensuring immutability and tamper-resistance of the entire storage index. During file retrieval, the system queries the blockchain to collect all block hash addresses, fetches encrypted blocks from IPFS, reassembles them in the correct sequence, and delivers the decrypted output to the user. The implementation is built using Python, Django, Web3.py, and the Truffle/Ganache development environment, and has been functionally verified across all core user-facing modules.
This paper introduces the Quantum Tensor Sequence (.qtsq) format, a universal file format built to compress any kind of data — whether it's images, audio, video, text, structured data, or raw binary. Instead of treating everything as a generic stream of bytes like most compressors do, .qtsq looks at what the data actually is before deciding how to compress it. An internal component we call the "Spaghettification Engine" analyzes the input, figures out which of 16 data types it belongs to, and picks the best compression strategy for it: Iterated Function Systems (IFS) for images, Discrete Fourier Transform (DFT) for audio and signals, dictionary-schema encoding for text and structured data, and procedural seed generation for binary data. Everything is packed into a single 80-byte header that supports 11 features: type-aware compression, lazy partial decompression, AES-256-GCM encryption, Schnorr zero-knowledge proofs, Reed-Solomon error correction, inter-file deduplication through wormhole links, compressed-domain differential updates, multi-resolution quality scaling, and adaptive size thresholds. The whole thing is organized around a five-layer architecture inspired by black hole physics — each region of the black hole maps to a stage of the compression pipeline. As far as we know, no existing file format brings all of these capabilities together in one place. Based on the theoretical properties of the algorithms involved, we expect compression ratios somewhere between 18:1 and 55:1 on mixed-type data. Real-world benchmarks using the reference implementation will follow in a separate paper.
Modern legal institutions encounter significant difficulties ensuring document security, public access, and verification processes in digital environments. This research presents an innovative framework combining distributed ledger technology with decentralized file systems to address critical vulnerabilities in traditional court record management. Our solution leverages Ethereum's smart contract capabilities alongside the InterPlanetary File System (IPFS) to establish an immutable, transparent, and distributed architecture for judicial documentation. The proposed framework demonstrates significant improvements in data integrity verification, unauthorized access prevention, and system resilience. Through comprehensive testing using authentic judicial datasets, we validated the system's capacity to detect tampering attempts while maintaining efficient document retrieval. Key contributions include: (1) a novel three-tier architecture integrating blockchain immutability with IPFS content addressing, (2) automated verification protocols through smart contracts, and (3) enhanced transparency mechanisms enabling public verification of document authenticity. Performance evaluations reveal substantial improvements in security metrics while maintaining acceptable operational efficiency. This research establishes a foundation for next-generation judicial information systems that prioritize transparency, security, and public trust.
Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage.
The InterPlanetary File System (IPFS) has been extensively promoted as a decentralized, censorship-resistant, and fault-tolerant storage protocol. This paper systematically dismantles these claims by demonstrating four critical and compounding vulnerability classes: (1) the structural dependency on centralized pinning services such as Pinata, Infura, and Web3.Storage, where compromising a single provider's dashboard or API effectively eliminates supposedly “immutable” content; (2) the futility of self-hosted pinning nodes as a mitigation strategy, given their susceptibility to targeted Distributed Denial-of-Service (DDoS) attacks capable of rendering them permanently unreachable; (3) the catastrophic implications of a cryptographic backdoor or collision discovery in SHA-256 or SHA-3 (Keccak), which would enable arbitrary content substitution while preserving valid Content Identifiers (CIDs), thereby destroying IPFS's fundamental integrity guarantees; and (4) the vulnerability of distributed pinning strategies to gossip-protocol-based reconnaissance attacks, wherein a state-level adversary (e.g., NSA, GCHQ, or equivalent) can enumerate all nodes hosting a target CID by compromising a single peer and leveraging protocol-level metadata propagation to systematically identify and neutralize every replica simultaneously. We formalize each attack vector with mathematical models, provide proof-of-concept algorithms, analyze the compounding effects of multi-vector attacks, and demonstrate that even the most sophisticated defense-in-depth strategies fail against a sufficiently resourced adversary. Our analysis conclusively establishes that IPFS, as deployed in practice, provides no meaningful censorship resistance and constitutes what we term Decentralization Theater—a system that employs the aesthetics and terminology of decentralization while maintaining the vulnerability profile of traditional centralized architectures, augmented by a dangerous false sense of security.
In highly dynamic and interference-prone environments, secure access to wireless communication spectrum and protection of user privacy present significant challenges. To address these issues, this paper proposes a novel framework called the Secure and Privacy-Enhanced Channel Transmission Architecture (SPECTRA). SPECTRA enables fine-grained access control policies defined by a trusted authority and enforces them through distributed Semi-Trusted Distributed Servers (STDSs) using Attribute-Based Encryption and Zero-Knowledge Proofs. This design allows for dynamic spectrum authorization without revealing the requester's sensitive attributes. Furthermore, the frequency-hopping process is modeled as a Markov Decision Process (MDP), and optimized through Proximal Policy Optimization (PPO) to adaptively enhance communication reliability and resistance to interference. SPECTRA also supports both secure replacement and secure incremental update mechanisms, incorporating hash aggregation and elliptic curve signature-based batch verification to improve the efficiency and security of multi-request authentication. Extensive simulations demonstrate that SPECTRA achieves high-accuracy access control, effective communication privacy preservation, and stable frequency-hopping performance under adversarial and uncertain conditions.
Saha Reno, Koushik Roy, G M Abdullah Al Kafi, Khandakar Md Shafin
ABSTRACT The simultaneous achievement of scalability, security and decentralisation remains an open problem for distributed ledger technologies. This paper introduces InternxtChain, a novel framework leveraging Internxt's decentralised storage infrastructure with zero‐knowledge proofs (ZKPs) and sharded proof‐of‐storage (SPoS) consensus. Specifically, erasure‐coded sharding ensures data availability and fault tolerance by splitting files into encoded fragments distributed across nodes; BLS‐381 aggregated signatures enable efficient consensus by compressing multiple signatures into a single short proof; and zk‐SNARK audits provide tamper‐evident storage verification without revealing user data. InternxtChain addresses this challenge through three synergistic mechanisms: (i) erasure‐coded sharding with (6,3) Reed–Solomon encoding, (ii) zk‐SNARKs for storage auditability and (iii) an SPoS consensus based on BLS‐381 aggregated signatures. Experimental evaluation on a testbed of 2048 nodes across 16 geographic regions shows that InternxtChain processes 2800 transactions per second (TPS) with a median latency of 420 ms, while maintaining 99.9% data integrity under up to 30% Byzantine nodes. These results establish a practical path toward harmonising Web3 principles with real‐world throughput, cost and General Data Protection Regulation (GDPR) auditability requirements.
Non-fungible tokens (NFTs) on Ethereum currently follow a binary mobility paradigm: ERC-721 enables unrestricted transfers, whereas SBTs (ERC-5192) prohibit transfers entirely. We identify a design gap in which no standard mechanism supports bounded transferability, where ownership mobility is allowed but limited to a finite number of programmable transfers. We study counted NFT transfers and introduce ERC-7634 as a minimal realization compatible with ERC-721. The design augments each token with a transfer counter and configurable cap L, allowing ownership to evolve under a finite transfer budget. ERC-7634 defines a minimal extension interface with three lightweight functions (transferCountOf, setTransferLimit, and transferLimitOf), two events, and native-transfer hooks, requiring fewer than 60 additional lines of Solidity while preserving full backward compatibility with existing NFT infrastructure. We analyze behavioral and economic consequences of counted transfers. Our results reveal (i) a mobility premium induced by remaining transfer capacity, (ii) a protocol-level costing signal that can deter wash trading in cap-aware markets through irreversible budget consumption, (iii) bounded recursive collateralization enabled by limited ownership turnover, and (iv) associated security and gas-cost implications, including wrapper-bypass trade-offs. Evaluation on calibrated simulations shows that moderate limits (e.g., L = 10) affect fewer than 15% of tokens under representative transfer distributions, while repeated manipulation becomes unprofitable after a few cycles in a cap-aware pricing model; the additional gas overhead remains below 11% per transfer. We further position ERC-7634 within the NFT mobility design space, derive practical cap-selection guidelines, and discuss post-cap ownership outcomes including soulbound conversion, auto-burn, and provenance freeze.