Yi-Jing Liu, L. Zhang, Xiaoqian Li, Hongyang Du · 8 authors
Integrated Sensing and Communication (ISAC) is driving the evolution of edge intelligence. In ISAC-enabled wireless edge networks, federated learning (FL) is crucial for realizing edge intelligence by supporting the networks with privacy protection, efficient data management, and dynamic adaptability. Specifically, FL allows distributed computing nodes (e.g., sensor devices) to first train local models by using data collected or sensed via ISAC and subsequently send them to one or multiple aggregation nodes for global model collaboration. However, traditional FL frameworks face significant challenges in the ISAC scenarios. For example, the privacy sensitivity of heterogeneous sensor data and the lack of transparency in model parameter exchange make it difficult to ensure the credibility of local and global models. Sharding distributed ledger technology (DLT), which divides the ledger into smaller and manageable shards, offers a potential solution to address these challenges by utilizing multi-node trust capabilities to facilitate distributed consensus during FL training. In this paper, we propose a trusted FL framework that incorporates sharding DLT within ISAC-enabled wireless edge networks to enhance both model training and consensus performance. Specifically, we develop a theoretical model to examine the interactions between model training performance and network capacities of sensing nodes (e.g., storage, computing, and communication capabilities) based on ISAC’s real-time channel state information. Based on this theoretical model, we design a trusted clustering scheme for aggregating local models. Numerical results demonstrate that in ISAC-enabled wireless edge networks, our proposed scheme significantly increases network throughput for model transmission while ensuring optimal model learning performance compared to some classical baselines.
In this paper, I prove that sublinear regret across the environment Class C requires six functional properties, that these properties are mutually independent, and that they compose into a directed informational chain closing back on itself — a six-link cycle whose final link is grounded in an explicit Doob martingale construction over cycles of play. All six properties are defined functionally — as conditions on the distributions a decision-maker induces over actions and canonical summaries — so the results are invariant under implementation and apply to any decision-making system that can be modelled within the class: a person, an institution, or a machine. Every theorem in this paper, without exception, is checked line by line in the Lean 4 proof assistant against Mathlib: the formalization (~12,700 lines) contains zero `sorry`, zero custom axioms, and zero opaque definitions. Class C is the union of all POMDPs satisfying at least one of six structural properties covering the fundamental qualitative dimensions of adaptive hardness: reward ambiguity (P1), absorbing traps (P2), local optima (P3), deterministic optimality (P4), constrained feasibility (P5), and nonstationarity (P6). * Part I (Necessity). I define six primitives X1–X6 as purely functional properties of decision rules: Objective Tracking, Cross-Context Safety Transfer, Global Attractor Exploration, Policy Simplification, Feasibility Projection, and Feedback Adaptation. For each, I construct an explicit environment in C and prove an unconditional Ω(T) regret lower bound for any decision-maker lacking that primitive.* Part II (Independence). For every ordered pair (i,j) with i≠j, I exhibit an explicit decision rule possessing Xj but lacking Xi that suffers Ω(T) regret on the matching environment. All thirty directed-pair results are shown to follow from one master theorem, verified on a single compound environment with full non-interference analysis.* Part III (Sequential Dependence). Necessity is domain-invariant — a structural failure is a structural failure no matter what "success" means to the decision-maker — which is why Parts I and II hold unconditionally. Sufficiency is not: what counts as success is supplied by the domain, not by the theorem, so a single closed-form sufficiency result covering every domain at once would have to either fix one arbitrary notion of success and stop being general, or say nothing of substance. Part III proves exactly what generalizes. I prove six Information Enhancement Theorems establishing that the six primitives compose into a directed information chain: possessing Xi strictly increases the mutual information available toward any goal variable at Xi+1's task. Each of the six links is established outright — a forward theorem, a reverse theorem, and a non-reversibility result — with the exact point where a domain's own definition of success enters the chain named explicitly, as an Implementation Obligation, rather than assumed away. The closing link, X6→X1, is grounded in an actual Doob martingale construction: given that the cycle-indexed posterior is a martingale, it converges almost surely to the truth across cycles — the precise sense in which the chain accumulates rather than resets. To this paper's knowledge, no prior formalization unifies this many independently-proven-necessary structural properties into a single machine-checked class with proven mutual independence across all of them. All mathematical work is provided in full transparency and independent verification is highly encouraged: the complete Lean formalization, with a passing build and every theorem cross-referenced to its exact identifier, is at github.com/M-Ismail-ZA/IsmailsPrimitives. For any feedback or collaboration, please contact me via the email address listed on the paper. Updated: 3 July 2026 (V6.1).
R Udayakumar, A Haja Alaudeen, Anjali Goswami, N Arvinth · 5 authors
Blockchain technologies incorporated with decentralized energy grids can offer new paradigms in energy management, distribution, and consumption. Trust building among participants in decentralized energy systems is a critical challenge in the absence of a central authority. Foremost, the self-executing, fully autonomous blockchain-based smart contracts and the decentralized automated systems enable real-time, secure, and transparent business transactions. This paper examines the domain of smart blockchain contracts in decentralized energy grids. Smart contracts facilitate self-enforcement, aiding trust-building. Invoice blockchain contracts enable automated energy exchange, thereby facilitating contract execution without third-party involvement. Trust-building, system decentralization, energy producers and consumers bypassing operators, and operational cost reduction for self-sustainability provide system autonomy. The paper also discusses self-governing blockchain contract systems engineering. In smart energy contracts, the paper outlines system challenges, which include load, interoperable infrastructure, and energy consumption. Empirical and case study research, which are the primary focus of the paper, present blockchain contracts for decentralized energy systems as a trust-management and transaction-integration technology.
The article examines the role of cryptocurrencies in the shadow economy, their specific features that create conditions for illegal financial transactions. The methods of using digital assets in criminal activities, including money laundering and illegal transactions, are analyzed. Special attention is paid to the problems of regulating the cryptocurrency market in Russia, new legislative initiatives and control measures. Technologies for monitoring blockchain transactions aimed at detecting suspicious transactions and reducing the risks of using cryptocurrencies in criminal schemes are also affected.
Romulo de Moraes, Arthur G. Bubolz, Denner Ayres, Vinícius Teixeira Pinto · 5 authors
Este artigo apresenta uma arquitetura para loterias descentralizadas na rede Ethereum, baseada em contratos inteligentes, aleatoriedade verificável e otimização de armazenamento por meio de Merkle Tree. A proposta visa reduzir o custo médio das transações (gas fees) e aprimorar a escalabilidade onchain, comparando três abordagens distintas de armazenamento: array, mapping e Merkle Tree. Os resultados mostram que o consumo de gas evidencia uma vantagem expressiva da Merkle Tree, reduzindo em até três ordens de magnitude o custo total, o que confirma sua eficiência e potencial para aplicações descentralizadas de alta demanda.
Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.
Open access
Cryptography and Data Security
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
André Augusto, Rafael Belchior, Jonas Pfannschmidt, André Vasconcelos · 5 authors
Cross-chain bridges are a blockchain interoperability middleware that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose XChainWatcher (Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using XChainWatcher, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. XChainWatcher successfully identified the transactions that led to losses of $611M and $190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (cctx) that should not have accepted, failed attempts to exploit Nomad, over $7.8M worth of tokens locked on one chain but never released on Ethereum, and $200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 cctxs across three blockchains, capturing more than $4.2B in token transfers.
While decentralized authentication mechanisms have gained significant attention for enabling user-centric identity management without centralized authorities, the critical counterpart - authenticated key exchange (AKE) in decentralized settings - remains understudied. Although it forms the basis for secure communication in decentralized scenarios, shifting existing AKE protocols to decentralized settings is impractical: the trust assumption is different, and the insufficient support for dynamic identity attributes, etc. To address these challenges, we present a novel decentralized AKE protocol that innovatively integrates attribute authentication with key exchange through multi-party secure computation. Building upon MPCAuth's foundational framework (S&P 23), our protocol goes further to provide key exchange based on authentication of real-world attributes such as a digital passport and email address, etc. Our protocol establishes a new paradigm for decentralized AKE without complex credential operations and heavy zero-knowledge proof. The core of our protocol is a distributed way to securely reconstruct the attributes and establish a session key. We further evaluate its performance across multiple servers. Experimental results on servers under 5 demonstrate that it can finish the full AKE procedure in an acceptable time, enabling efficient and scalable multi-party key AKE in distributed environments.
Anurag Anand Duvey, Chandrashekhar Goswami, Amit Kumar Goel
Federated Learning (FL) gives opportunity to decentralized model training without the raw data's revealing. But in actual real-world implementation faces certain number of challenges. These include trust in client updates, verifiable end-to-end privacy promises, equitable contributor compensation, and accountable aggregation. This paper provides a design-first architecture that addresses these issues by integrating concise zero-knowledge proofs (zk-SNARKs) for trustless verification with smart-contract arrangements. This approach is for verification, secure aggregate pooling, and reward settlement. Our design is consisting a structure of five-layer stack, named as Client, Proof, Blockchain, Incentive, and Governance. It highlights end-to-end workflows for the generation of proof for updates, verifying them on chain, anchor-off chain aggregation sequence anchoring, and allocate contribution-matching token payouts. We specify clearly smart-contract interfaces called as aggregation, registry, incentivization, zk-circuit objectives, and several scaling controls like proofs/aggregated in batch or Layer-2/rollup rollouts and the anchor-Merkel. The design also implements Shapley-like contribution measures and ERC-compact reward settlement. It incorporates Sybil-resistance and vesting primitives to lean against game. We define a crisp threat model, discuss security and privacy trade-offs, and suggest evaluation using healthcare and IoT benchmarks. These assess the learning utility, resilience to poisoning, fairness of the payouts, and system costs in terms of gas fee. Moreover, by training and incentivization by smart contracts at design level, we create a foundation for future prototyping and rigorous empiric testing. This sets a realistic path from the design framework to effective workable, end-to-end Privacy-preserving Federated Learning deployment.
Assets security is a critical subject for employers because it decides the fate of organizations. If the employers fail to control the asset management, then they face drastic losses. Organizations use several strategies for controlling the asset management in operations management. However, they got outdone in maintaining secure asset management, and it may result in drastic losses. Industry personnel are worried about asset management in decentralized stores as compared to centralized stores because there are a lot of foul play results that occur by willingly, fully, or intentionally attempting. These attempts can be reduced by vigilant and real-time information plans, but they can never be eliminated. Finance department analysis showed that the most minor malfunction deteriorates the operations management and could lead to an industry downturn. The present study proposed a secure decentralized architecture for operations management excellence using the blockchain concept. The architecture provides secure assets management in decentralized stores in operations management in Industry 3.0 and Industry 4.0 environment. The proposed architecture resolves the challenges faced by industry personnel in establishing different decentralized stores on the shop floors. Decentralized stores play a vital role in achieving desired operations outcomes because they decrease the material handling and breakage possibilities but increase insecurity chances. The proposed architecture helps in tackling issues faced in controlling decentralized stores by implementing blockchain concepts in Industry 3.0 and Industry 4.0 environments.
Federated Learning (FL) enables multiple participants to collaboratively train a shared model without sharing their private data. However, FL remains vulnerable to malicious clients submitting incorrect updates to disrupt training. To address this, we formalize each client’s local training step as a Nondeterministic Polynomial-time (NP) statement, verifiable via zero-knowledge proofs (ZKPs) at every round. We propose Jolt-FL, the first general-purpose verifiable FL framework that immediately detects and excludes malicious clients upon their first dishonest action – without relying on heuristics, statistical assumptions, or multi-round analysis. Built on Jolt’s zkVM, a state-of-the-art zero-knowledge virtual machine (zkVM) developed by a16zcrypto, Jolt-FL guarantees training integrity and data privacy without trusted hardware or third-party intermediaries. By witnessing every computation step, it defends against a wide range of attack vectors, securely filtering dishonest updates even if up to 50% of clients are malicious, while preserving convergence and final model performance. To demonstrate feasibility, we implement a prototype featuring a complete end-to-end Convolutional Neural Network (CNN) for image classification using the MNIST dataset. To our knowledge, this is the first fully verifiable end-to-end CNN training under ZKPs without any custom circuit design. Our solution achieves competitive proof generation times, compact proof sizes, and low verification costs–all while preserving model accuracy on par with standard FL.
Saudi Arabia’s rapid urbanization driven by Vision 2030 demands sustainable municipal finance systems. Using a mixed-methods analysis, this study analyzes 360 expert perspectives and identifies the key challenges of fiscal centralization (β = –0.14), governance deficits (20.2% variance), and overreliance on centralized funding (31.8% variance). However, decentralization (β = 0.31), policy alignment with Vision 2030, and green finance tools emerge as transformative pathways. Regression and correlation analyses reveal that municipal autonomy and legal frameworks are crucial in promoting sustainability integration. This study advocates for fiscal decentralization, Sharia-compliant green bonds, and institutional reforms and offers useful insights for policymakers.
Johnny Y. Solano Marinho, Eryk Schiller, Arthur Debauge, Noria Foukia
This paper presents a framework that integrates the Coalition for Content Provenance and Authenticity (C2PA) standard with Zero-Knowledge Proofs (ZKPs), specifically the Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs), to enable verifiable yet privacy-preserving authentication of digital images. Using the ZoKrates toolkit, the system derives a non-revealing fingerprint from the image, generates a succinct proof of integrity, and embeds this proof into C2PA-compliant metadata without exposing the underlying content. The proof can be verified locally or on the Ethereum blockchain using a Groth16 smart contract verifier, providing decentralized and auditably transparent validation. This capability allows journalists, victims, and legal professionals to attest to the existence and integrity of sensitive evidence while deferring its disclosure. Experimental results show that proof verification is highly efficient, requiring approximately 0.01 s, and that the entire workflow is reproducible within containerized environments. The proposed integration of zk-SNARKs with C2PA establishes a practical foundation for secure digital provenance, privacy-preserving evidence management, and strengthened trust in digital media ecosystems.
Purpose The evolution of the cryptocurrency landscape has been innovative, dynamic and adaptable. Using performance analysis and science mapping techniques, the study aims to conduct a bibliometric analysis to examine the landscape of the cryptocurrency domain, focusing on the forecasting aspect. Design/methodology/approach The study uses metadata from the Scopus database, ranging from 2015 to 2024, comprising 849 articles. They identified significant research constituents and five major thematic clusters. Findings The findings suggest that the research in the domain has yet to reach its full potential. The clusters involve structural shifts or turbulence in cryptocurrency markets, machine learning-based cryptocurrency price prediction, forecasting Bitcoin price and volatility, Bitcoin returns analysis, and cryptocurrency: A hedge and safe haven alternative. Further empirical analysis revealed that the artificial neural network and deep neural network outperformed the traditional statistical model, the autoregressive integrated moving average (ARIMA). Originality/value The study supplement these findings with significant future research directions, which will be beneficial for upcoming studies as the field has immense potential and countless areas worth exploring.
Abstract The accelerating dominance of non-human agents in digital infrastructures has created an existential imbalance between human intentionality and synthetic computation. All identity-centric and post-hoc verification paradigms have failed against advanced automation. This work introduces Proof of Being (PoB) — an ontological cryptographic primitive that binds digital agency to continuous, embodied human presence without revealing identity. Using Human Intention Semantic Proof Units (HISPU) — the fusion of physiological dynamics, semantic activity structure, and changing environmental context — we generate zero-knowledge proofs of authentic human engagement. From this substrate emerges the Vital Presence Token (VPT), a new energy-like digital asset that supplies “existential energy” exclusively to human-authorized agents. Ontological Digital Agents Management (ODAM) implements a biological-immune-system analogue: agents lacking fresh VPT undergo ontological death and cannot claim computational resources. The framework establishes verifiable human presence as the constitutional substrate for post-AGI digital civilization and defines the economic foundations of Web4. Keywords: proof of being, ontological cryptography, human presence verification, digital immune system, vital presence token, Web4, post-AGI governance, zero-knowledge biometrics, existential energy, digital agents management, human-in-the-loop, semantic intentionality, biological computing, HLA/MHC analogy, human sovereignty, scarcity, decentralized identity, ODAM, HISPU.
Martin Christy ABIAYA'A, Tati Gaelle TIMBA, Jean Hugues NLOM, Marcellin NDONG NTAH
Abstract The objective of this article is to analyze the effect of fiscal decentralization on early childhood education in Cameroon. Using a methodological framework based on econometric modeling by ordinary least squares (OLS), generalized least squares (GLS), and the generalized method of moments (GMM), it emerges that fiscal decentralization positively and significantly affects early childhood education in Cameroon. The results obtained by OLS and GLS reveal that Fiscal decentralization has a significant and positive effect on the number of desks per student. The Global Monitoring Mechanisms (GMM) demonstrate that fiscal decentralization leads to a significant and positive increase in both the number of classrooms per student and the number of desks per student. The investigations revealed that fiscal decentralization has a positive effect on early childhood education in Cameroon.These results suggest implementing financing mechanisms for local authorities to stimulate local development through the provision of sustainable socioeconomic infrastructure that can ensure equal and equitable access to education for children. Keywords: Cameroon, schooling, early childhood, fiscal decentralization
Abstract - The paper referenced proposes a decentralized marketplace model for trading, verifying, and managing the ownership of AI models by means of blockchain and NFTs. Given the need for trusted exchange and provenance in the management of AI assets, the authors propose a system wherein AI models and datasets are represented as NFTs on a public blockchain, providing transparent, traceable, and secure transactions. Smart contracts automate auctions, royalty distributions, and ownership transfers. Further security and privacy are provided by TEEs, proxy re-encryption, and decentralized storage (IPFS). Collaboration is enabled through the architecture, which allows contributors to improve and resell models, while royalty schemes guarantee fair compensation for creators. Details of the implementation include smart contracts in Solidity and cost analyses for transaction efficiency. Evaluation in terms of security is resilient against Sybil and Eclipse threats. It is also set up as broadly adaptable to both public and private AI assets and easily generalizable to other situations of digital assets to ensure robust provenance, fair remuneration, and trustless exchange. Key Words: Blockchain, Non-Fungible Tokens(NFTs), Decentralized AI marketplace, Smart Contracts, Digital Ownership
National identity systems require efficient, equitable decision-making that safeguards personal data. This article proposes a Self-Sovereign Identity (SSI) architecture, supported by a Verify-Without-Reveal (VWR) framework, designed for national-scale implementation. SSI places credentials in a citizen wallet and enables selective disclosure and zero-knowledge proofs, so services can verify attributes without seeing underlying records. VWR adds the policy and accountability spine: yes/no attribute APIs for holder-absent cases, purpose-bound and zero-trust enforcement on every call, and an immutable audit layer on a permissioned ledger. The study synthesises current standards and leading implementations in Europe and worldwide and formulates a deployable blueprint with clear roles, consent and lawful-override flows, per-agency pseudonyms, and regulator and citizen visibility. The study outlines reference APIs, user experiences for wallets and verifiers, and performance metrics suited for national workloads. Privacy-preserving AI strengthens biometric liveness, fraud detection, and anomaly response without centralising sensitive data. The framework aligns with GDPR data minimisation and purpose limitation, supports the European Digital Identity Wallet, and meets high-risk AI governance requirements. Results show how SSI proofs and VWR controls reduce unconsented disclosure and cross-agency browsing, while keeping latency low and interoperability high. The contribution is both conceptual and operational: a phased migration path that turns verify-without-reveal into the default mode for government and regulated services, improving security, inclusion, and public trust.
The Internet of Vehicles (IoV) is rapidly evolving to provide high throughput, low bandwidth, and enhanced security for high-quality information transfer with minimal latency. However, before effectively adopting all these services, IoV faces various hurdles, including secure information sharing, tampering with information, and network overhead. IoV with blockchain provides substantial advantages for secure information sharing, tamper-proof storage of information, and efficient network administration. We propose EtherChain, a blockchain-based secure approach for IoV to provide information integrity, tamperproof distributed storage, and efficient network administration. EtherChain uses transaction validation, trust, and miner selection algorithms to verify transactions, manage trust between nodes, and select the miner to append the block to the blockchain. Following that, EtherChain compares security strength in terms of several forms of security attacks with other cutting-edge techniques, as well as performance outcomes in terms of throughput, cryptography operation time, and communication overhead. The research demonstrates that EtherChain outperforms state-of-the-art solutions in terms of performance and security.
The rapid growth of Industry 4.0 has transformed traditional manufacturing systems into connected cyber-physical environments. These systems continuously produce large amounts of operational and sensor data, which are vital for predictive maintenance and informed decision-making. However, the reliability, integrity, and security of this data remain significant challenges. This paper introduces a blockchain-based industrial monitoring framework that integrates Artificial Intelligence (AI) for predictive analytics with blockchain for decentralized and tamper-proof data management. The framework enhances transparency, traceability, and secure collaboration among stake-holders by maintaining verified records of equipment health and maintenance activities. Smart contracts also automate fault alerts, compliance checks, and maintenance scheduling, removing the need for centralized authorities. Experimental results on industrial datasets demonstrate better data integrity, lower risk of cyber threats, and improved predictive accuracy. The proposed hybrid architecture offers a scalable, auditable, and secure foundation for next-generation industrial systems.
The rapid growth of blockchain technology and the increasing number of network nodes have heightened the risk of sophisticated attacks. Among these, Eclipse attacks present a serious threat to decentralized networks by exploiting their peer-to-peer structures. While previous research has explored artificial intelligence techniques to defend against Eclipse attacks, evolving attack patterns continue to challenge existing defenses. In this paper, we propose a novel defense framework that integrates a clustering approach based on self-attention encoders within a multi-kernel neural network clustering model. Our method utilizes parallel subnetworks to extract category-specific features from multiple perspectives, generating discriminative cluster centroids that are combined with raw transaction data to train a robust classifier for detecting Eclipse attacks in Ethereum networks. To evaluate our approach, we simulate Eclipse attacks on the Ethereum testnet and conduct extensive experiments. The results demonstrate that our method achieves a detection accuracy of 98.5% and improves classification performance by 5% compared to models trained without cluster-enhanced features, confirming the effectiveness of the proposed defense.
The digital transformation of education necessitates secure, private, and learner-centric methods for verifying academic credentials. Conventional verification processes expose sensitive personally identifiable information, creating privacy risks that conflict with data protection regulations like GDPR. Existing blockchain solutions for educational credential verification face persistent challenges including prohibitive transaction costs, privacy vulnerabilities, and inflexible verification models. This paper presents VeriZKP, a proof-of-concept architecture demonstrating gas-free credential verification on Ethereum using zero-knowledge proofs. The core innovation lies in separating on-chain trust anchoring from off-chain cryptographic computation, enabling a novel cost-elimination mechanism. The system leverages Ethereum’s view functions through pre-compiled verifier contracts to achieve zero gas consumption for verification operations while preserving privacy through selective disclosure mechanisms. Our prototype, evaluated on Ethereum Sepolia testnet, validates the fundamental feasibility of this approach. Results demonstrate complete elimination of verification costs, practical client-side proof generation times of 1.02-1.63 seconds on standard hardware, and support for multi-attribute credential verification. The architecture proves both economically viable and performant for blockchain-based identity systems.
Reaching consensus in Proof-of-Stake (PoS) based consensus protocols, requires supermajority agreement among participating validator nodes. Such protocols need significant network resources due to the concurrent voting of a large number of consensus nodes. As a solution, these nodes are divided into committees, with each committee voting individually at a dedicated time slot. In this paper, we introduce CliqueSensus, a protocol that, given a distribution of consensus nodes into committees, lets them self-organize into small, ephemeral clusters structured in clique topologies, to accelerate the voting process, while using only a small fraction of the network resources required by conventional message dissemination methods. Our evaluation demonstrates that our protocol exhibits rapid convergence and operates with minimal network overhead. We focus on the PoS consensus algorithm adopted by Ethereum 2.0. In addition to our protocol, we also analyze and simulate the clustering approach that Ethereum has adopted, showcasing that our protocol can reduce validation message dissemination time by 23% to 70%, while requiring about 190 times fewer validation message forwards.