The advent of sufficiently powerful quantum computers poses an existential cryptographic threat to elliptic-curve-based public key infrastructure, upon which major blockchain networks depend for transaction security and identity. This paper conducts a rigorous comparative analysis of quantum risk exposure for Bitcoin and Ethereum, examining the structural, governance, and economic dimensions of post-quantum cryptographic (PQC) transition for each protocol. We analyze the mathematical incompatibility of leading NIST standardized PQC signature schemes with current blockchain scalability constraints, with particular attention to signature size inflation (30-100× current schemes), the loss of algebraic linearity preventing signature aggregation, and the resulting implications for block space, fee markets, node economics, and validator infrastructure. We subsequently contrast Ethereum's upgrade-oriented, stake-weighted governance model and its modular cryptographic architecture against Bitcoin's deliberately ossified, consensus-driven governance structure. Our findings indicate that while Ethereum possesses the structural and institutional prerequisites for a credible, phased transition to post-quantum cryptography, Bitcoin's governance model and architectural constraints render such a transition highly contested and potentially irresolvable without chain fragmentation. We conclude that Bitcoin's structural limitations, compounded by deep ideological fractures and the irreversible nature of PQC deployment, place it at significant risk of prolonged governance stagnation or chain split, undermining its position as a reliable store of value and 'digital gold' standard in the medium term.
This study examines how funding rate regimes affect the downside risk contribution of cryptocurrencies in equity portfolios. The research focuses on Bitcoin and Ethereum as the two largest cryptocurrency markets and evaluates whether downside risk differs across market conditions defined by perpetual futures funding rates. The objective is to connect cryptocurrency derivatives market conditions with portfolio downside risk assessment, as earlier literature has mainly examined these topics separately. The empirical analysis uses daily data from 1.1.2020 to 31.12.2025, and the data consists of returns for MSCI World Index, Bitcoin, Ethereum and perpetual futures funding rates. Research portfolios are constructed by adding cryptocurrency allocations of 5%, 10%, 15% and 20% to the equity benchmark portfolio. Downside risk is evaluated using historical Value-at-Risk, historical Expected Shortfall and Maximum drawdown. Downside risk is examined both over the full sample and separately across funding rate regimes. Funding rate regimes are classified into low, neutral and high conditions. The results suggest that adding cryptocurrency exposure increases downside risk relative to the equity benchmark across all portfolio groups. Funding rate regimes reveal meaningful differences in these results, but the effects differ between assets. Bitcoin portfolios show the clearest and most consistent regime dependence, with the most severe downside risk in the low funding regime and the mildest downside risk in the high funding regime. Ethereum portfolios show weaker and less stable regime separation. The results provide some indication that high funding conditions in Ethereum may be associated with less frequent but more severe tail losses. Specifically, high ETH funding environments appear to be associated with relatively mild Value-at-Risk results but more severe Expected Shortfall outcomes. Mixed Bitcoin-Ethereum portfolios produce strong statistical separation between regimes, with neutral funding conditions consistently associated with the mildest downside risk results. The findings suggest that derivatives market conditions may provide useful information when assessing downside risk of cryptocurrencies in equity portfolios. Funding rate conditioning appears particularly informative for Bitcoin downside risk assessment, while Ethereum results suggest that funding conditions may affect the structure of tail losses differently across market environments. The study contributes to existing literature by introducing a regime-conditional framework for evaluating downside risk under changing cryptocurrency derivatives market conditions.
Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja investasi Bitcoin, Ethereum, emas, dan Indeks LQ45 selama periode 2020-2024 dilihat dari sisi return, risiko, dan rasio Sharpe. Data yang digunakan merupakan harga penutupan bulanan yang diperoleh dari situs resmi investing.com. Metode analisis yang digunakan yaitu uji ANOVA dilanjutkan dengan uji lanjut post-hoc Tamhane’s T2 dan Tukey HSD. Hasil penelitian menunjukkan bahwa secara agregat terdapat perbedaan return antar instrumen, namun perbedaan tersebut tidak signifikan secara statistik pada uji post-hoc. Risiko merupakan pembeda utama dalam perbandingan keempat instrumen, dengan Ethereum sebagai aset paling berisiko, disusul oleh Bitcoin, Indeks LQ45, dan emas sebagai aset paling stabil. Pada rasio Sharpe, hanya terdapat perbedaan antara aset Bitcoin dan Ethereum dengan Indeks LQ45, di mana Bitcoin dan Ethereum menunjukkan efisiensi kinerja lebih baik dalam menghasilkan return terhadap risiko dibanding Indeks LQ45.
This thesis investigates how GossipSub configuration choices influence data dissemination for a Data-Availability Sampling (DAS) workload under strict consensus time bounds. Using a configurable PeerSim-based simulator, we model a FullDAS-like setting in which a block producer erasure-codes a blob into a 2D extended matrix of share segments, parti- tions row/column segments into topics (custody-style sharding), and executes a two-phase workflow: seeding, where share segments are disseminated over topic meshes, and sampling, where validators must retrieve uniformly random share segments within a T_DAS = 4 s deadline. We systematically vary topic granularity (TOPICS), segmentation (segment amount, SA), replication (K-copies), bandwidth caps, and omission fault rate α, and measure phase success rates, completion-time distributions (with emphasis on tail latency), bandwidth consumption, and duplication overhead. The results show that segmentation and replication dominate performance and overhead: increasing SA from coarse to moderate values reduces duplication with diminishing returns beyond SA ≈ 1-16, while larger K increases redundancy and overhead and mainly provide a robustness margin under adverse conditions. Seeding completes quickly and remains resilient for SA≥ 4 even at high omission, whereas sampling is tail-latency dominated and degrades more sharply as α increases, leading to widespread deadline misses near α = 0.5. Based on these findings, we adopt TOPICS= 256, SA= 8, K = 4, and a conservative per-node bandwidth cap of 60 Mbit/s for faulted multi-slot experiments to isolate GossipSub dynamics from bandwidth saturation.
The swift expansion of IoT devices in smart cities demands decentralized and open systems of attentive exchange of assets in automotive supply chains. Nevertheless, the majority of the available blockchain-based solutions are focused on traceability and ignore scalability, conditional payment automation, and real-time IoT verification. To overcome those pitfalls, the research proposes a Blockchain-based framework implemented on Hyperledger Fabric, incorporating Non-Fungible Tokens, and escrow-based smart contracts, to facilitate verifiable, automated vehicle transactions. The payment is conditionally released, and the vehicle is represented as a discrete NFT that undergoes authenticated release under Fabric Certificate Authority with escrow verification. Sub-millisecond latency (0.0003 s), constant throughput, and minimal computational cost experimentally verify the effectiveness of the framework in terms of its efficiency, privacy, and scalability in the efficient and autonomous exchange of assets in next-generation smart cities.
The rapid increase in fraudulent reproduction and misuse of digital certificates has become a critical concern for organizations and institutions worldwide. Fake or tampered certificates are often used to obtain employment in domains where individuals lack the required qualifications, thereby compromising organizational credibility and posing significant risks, particularly in sensitive sectors such as healthcare. With the proliferation of online learning platforms, certificates are issued digitally, making them vulnerable to unauthorized access, duplication, and identity forgery. To address these challenges, this paper proposes a secure and sustainable framework for proof of ownership of valuable educational assets using blockchain technology. Leveraging the capabilities of non-fungible tokens (NFTs), the proposed system ensures that each certificate is uniquely identifiable, tamper-proof, and verifiable. Unlike fungible digital assets, NFTs represent immutable and distinct records on the blockchain, enabling transparent and decentralized ownership verification. The proposed approach not only enhances trust and authenticity in educational credentials but also demonstrates applicability across multiple domains, including healthcare, supply chain, and digital asset management
Sara Migliorini, Mauro Gambini, Veronica Paternolli, Mila Dalla Preda
In the socio-economic landscape, we can recognize an emerging form of organization: the network coalition. A network coalition is a form of concerted cooperation in which a group of agents decides to collaborate to achieve a common goal. The decentralized and cooperative nature of a network coalition presents new challenges in automating its processes, which cannot be treated as traditional business processes managed by a centralized information system. In the literature, the notion of Exogenous Business Process (XBP) has been introduced to capture such processes. An XBP specification is intended as a potentially incomplete, renegotiable, procedural contract to which different parties adhere in order to achieve a predefined business goal. The concept of Enforceable Business Process (EBP) has been proposed as a possible abstraction for modeling and automating the XBPs of a network coalition. An EBP is essentially an evolution of the original concept of smart contract, which is able to properly manage contractual incompleteness while providing automatic enforceability. The aim of this paper is to formalize the notion of EBP and discuss how contractual incompleteness can be handled by a Decentralized Autonomous Information System (DAIS). The complete formalization of both procedural and incompleteness aspects of EBPs, as provided by this paper, lays the basis for the construction of the next generation of DAIS, as well as the diffusion of network coalition into many application domains, such as supply chains, business alliances, joint ventures, and others.
Ronald A. Balarezo Acuña, Gloria Susana Vizcaíno Cárdenas
This study aimed to determine employees’ perceptions of the organizational climate at the Municipal Decentralized Autonomous Government (GAD) of Salcedo Canton. A quantitative methodology with a descriptive and analytical design was applied. Data were collected using the Organizational Climate Questionnaire developed by Litwin and Stringer (1968), which consists of 50 items distributed across nine dimensions: structure, responsibility, reward, risk, warmth, support, standards, conflict, and identity. Responses were measured using a Likert-type scale, allowing for a detailed assessment of employees’ perceptions of different organizational aspects.The results indicate that, despite the presence of a relatively balanced sociodemographic profile among employees, the overall perception of the organizational climate is unfavorable. Notably, dimensions such as structure, warmth, support, and identity exhibited lower scores, suggesting deficiencies related to role clarity, recognition, interpersonal relationships, and organizational cohesion. Furthermore, the dimensions of identity and conflict reflected a weak sense of belonging and limited effectiveness in managing internal disagreements, which may negatively influence teamwork and institutional stability.Factor analysis revealed that a substantial proportion of the total variance was concentrated in a single dominant component, confirming the existence of a generalized negative perception of the organizational climate. In conclusion, the organizational climate of the Municipal GAD of Salcedo is characterized by the convergence of structural and psychosocial challenges that adversely affect employee motivation and commitment. Therefore, the findings emphasize the need to strengthen leadership practices, enhance communication processes, and implement organizational strategies that promote trust, recognition, collaboration, and a stronger sense of institutional identity.
Rajasekaran P., Duraipandian M., Johny Renoald Albert, R. Jamuna · 5 authors
The Internet of Medical Things (IoMT) in the IoT with Cloud Healthcare (CHI) creates a high volume of real‐time medical data, but traditional compression methods suffer high computation costs, privacy leaks and quantum attacks, while advanced cryptographic algorithms such as homomorphic encryption are costly and have poor scalability for the real‐time system application. In this work, we propose a quantum‐enhanced zero‐knowledge healthcare compression network (QZ‐HCN) that associates zero‐knowledge proofs (ZKPs) with quantum‐inspired deep learning (QIDL) by introducing an innovative adaptive quantum‐supported ZKP verification mechanism (AQ‐ZKV) and a quantum fusion autoconventional neural network (QF‐AutoCNN) technique to achieve efficient, privacy‐preserving compression. For healthcare IoT datasets, QZ‐HCN can reach 98.16% in accuracy, 97.09% in F‐measure, 96.32% in precision and 97.45% in recall, with a throughput of 449.57 bits/s; processing time is reduced to 0.85 s, and memory cost is minimised to be only 192 kbits, which outperforms CNN‐Encryption (90.23% accuracy), proxy re‐encryption and homomorphic encryption by at most 13 percentage points in accuracy and 75 percentage points in memory efficiency. The secure and scalable management for CHI data is achieved by QZ‐HCN, which solves the problems of privacy threats and space costs of real‐time medical applications.
haojia hong, Duan Jia, Wei Ren, Xi Liu · 5 authors
Proxy contracts support logic upgradeability and deployment reuse in decentralized application (DApp). However, the use of proxy mechanisms also introduces new security risks. Although prior work has explored the security of proxy contracts, most prior approaches either depend on source code or fail to handle complex proxy storage layouts at the bytecode level, reducing their applicability to real-world closed-source contracts. In this paper, we propose ProxyLens, a bytecode-level vulnerability detection framework tailored for Ethereum proxy contracts. Without requiring source code, ProxyLens integrates storage structure modeling, proxy pattern recognition, and vulnerability detection. It features (1) a slot recovery mechanism based on symbolic execution and taint analysis for precise variable layout inference, (2) a proxy identification and delegate slot tracing module for accurate proxy architecture recognition, and (3) a semantic-driven vulnerability detection engine that works independently of function names. Experiments show that ProxyLens significantly outperforms existing tools in storage slot recovery (F1 = 96.4%), variable type inference (accuracy = 63.0%), proxy pattern recognition (accuracy > 90% across multiple patterns), and vulnerability detection (e.g., function selector collision F1 = 98.77%, storage collision F1 = 79.50%). As a supplementary contribution, we have publicly released the prototype of ProxyLens.
The growing demand for digital access to precious metals has created opportunities for ownership systems that are transparent, secure, and globally accessible. This article presents a blockchain-based silver tokenization framework enabling customers to buy, hold, and redeem physical silver through verifiable on-chain records. A hybrid token model combines fungible tokens for value representation and nonfungible tokens for bar certificates. After building the core smart contract architecture, the next step is deployment. Given the growing number of platforms, identifying the most cost-efficient option is essential. This article compares six Ethereum virtual machine-compatible blockchains and evaluates them based on gas fees and operational cost.
Muhammad Faruq, Rahmad Abdillah, Nazruddin Safaat H., Pizaini
Smart contract vulnerabilities, particularly reentrancy, have caused hundreds of millions of dollars in losses across the Ethereum ecosystem. While static analysis tools dominate current auditing practice, empirical evaluations have consistently demonstrated their high false negative and false positive rates for reentrancy detection. Dynamic analysis, exemplified by property-based fuzzing with Echidna, offers an alternative by evaluating contracts through actual execution. However, systematic empirical evaluation of dynamic tools under controlled ground-truth conditions remains limited. This study adapts the bug injection methodology, previously applied only to static analysis evaluation, to assess Echidna's effectiveness in detecting reentrancy. A dataset of 50 Solidity contracts was instrumented with oracle properties and injected with two reentrancy variants, single-function and cross-function, producing 100 ground-truth contract variants. Three fuzzing configurations of increasing intensity were evaluated across three metrics: detection rate, activation rate, and average detection time. Results show that Echidna achieved 100% activation but detected only 20% to 42% of injected bugs depending on the configuration and variant. Nearly all detections occurred within the first 25 seconds of each campaign, with no benefit from extended timeouts. These findings reveal a fundamental gap between bug reachability and exploitability confirmation under standard fuzzing conditions.