With the acceleration of urbanization and the promotion of the âdual carbonâ goal, the road transport system is facing the triple challenges of efficiency bottlenecks, excessive carbon emissions, and data security risks. In view of the shortcomings of the existing research in dynamic response, multi-objective collaboration and privacy protection, this paper proposes a three-in-one intelligent management framework: (1) construct a real-time dynamic path optimization model based on Deep Reinforcement Learning (DRL), and realize the precise regulation of traffic flow through multi-source data fusion and adaptive reward mechanism; (2) Design a multi-objective optimization model integrating carbon trading mechanism to quantify the synergistic relationship between transportation efficiency, carbon emissions and economic costs; (3) Develop a distributed data management framework based on blockchain, and use zero-knowledge proof and smart contract technology to protect user privacy. The peak simulation experiment based on the fifth ring road section of Beijing shows that the proposed method reduces the average traffic time by 18.7%, the carbon emission by 23.5%, and the risk of data leakage by 76% compared with the traditional algorithm. This study provides theoretical and technical support for the construction of a safe, efficient and low-carbon intelligent transportation system.
The deployment of distributed digital twin systems in sectors such as healthcare, manufacturing, and critical infrastructure has significantly heightened the importance of data privacy. These systems interact with numerous devices and users, increasing the risk of data leakage or unauthorized access to sensitive information. Traditional centralized identity management and access control mechanisms no longer meet the scalability, autonomy, and privacy requirements of modern distributed architectures. This article explores how smart contracts operating in blockchain environments can provide decentralized access management for digital twin systems. Smart contracts enable transparent and reliable enforcement of access policies without relying on centralized authorities. The study examines the integration of modern cryptographic technologies into smart contract workflows, including zero-knowledge proofs, decentralized identifiers (DIDs), and confidential computing. These technologies make it possible to verify access rights and perform secure operations without revealing sensitive data. The article also analyzes the limitations of existing solutions, such as the high transaction costs of public blockchains, the limited performance of traditional smart contracts, and the challenges of integrating confidential computing into resource-constrained devices. The authors outline future research directions, including optimizing Layer 2 architectures to improve performance, developing secure auditing mechanisms, and ensuring compatibility with self-sovereign identity systems. The conclusions emphasize that privacy should be treated as a fundamental property of digital twin systems. In these environments, smart contracts must serve not only as governance logic but also as trusted agents that guarantee compliance with access policies and regulatory requirements in decentralized ecosystems.
Sustainable trade requires verifiable, granular, and trustworthy data across multi-jurisdictional supply chains. This paper argues that blockchainâs binding constraints are institutional, not technical, and proposes the Green Trade Blockchain Governance Trilemma: no design can simultaneously maximize (i) transactional efficiency, (ii) regulatory verifiability, and (iii) decentralized governance with commercial privacy. Comparative casesâTradeLens, IBM Food Trust, Everledger, and Power Ledgerâshow divergent institutional choices and outcomes: TradeLens faltered under perceived hegemonic control; Food Trust succeeded via a buyer mandate; Everledger thrived through symbiosis with trusted authorities; Power Ledger scaled within a regulatory sandbox. We further analyze the Oracle Problem as the key limit to verifiability and assess privacy-enhancing technologies, especially zero-knowledge proofs, as partial mitigations that protect sensitive data while enabling compliance checks. We conclude that success hinges on context-specific institutional designâcertified oracles plus verifiable computationârather than a one-size-fits-all stack, offering actionable guidance for policymakers, consortia, and firms building credible green-trade infrastructure.
The article addresses the issue of ensuringconfidential exchange of personal data in inter-organizationalinformation systems under conditions of increasing digitalinteraction between public and private sector entities. It is notedthat centralized models for processing and exchanging personaldata fail to provide an adequate level of protection againstunauthorized access, transaction tampering, and do not ensuresufficient transparency of data operations. These limitationshinder full compliance with regulatory requirements, particularlythe provisions of the General Data Protection Regulation(GDPR), ISO/IEC 27001 and 27701 standards, as well asnational legislation on information protection. The study substantiates the feasibility of using a permissioned blockchain as the architectural basis forimplementing a secure, decentralized exchange of personal datawith guaranteed access control, transaction audit, and dataimmutability. A conceptual model of the information system isproposed, involving smart contracts for managing data subjectconsent, access control, and the integration of the InterPlanetaryFile System (IPFS) for robust off-chain data storage. The modelalso includes the use of Zero-Knowledge Proof (ZKP) cryptographic mechanisms and behavioral verification criteriafor transactions. Particular attention is given to risk analysis associated withpersonal data processing in inter-organizational environments, and to the application of supplementary protection toolsâsuchas masking, pseudonymization, and data perturbationâtomitigate potential losses in the event of data leakage. A set oftechnical and organizational compliance criteria withinternational and national information security standards isoutlined. The aim of this research is to design an architectural modelfor inter-organizational personal data exchange based onpermissioned blockchain that ensures confidentiality, integrity, controlled access, and regulatory compliance in the field ofinformation protection.
Securities trading systems have settlement efficiency, audit transparency, and fraud prevention concerns due to centralized intermediaries and aging infrastructure. Existing research models risk counterparty trading due to delayed settlements, opaque record keeping, and human compliance checks. The study aims to design and evaluate a blockchain-based equities trading platform for transaction security and traceability. Provable Atomic Consensus for Trading (PACT), a blockchain-based architecture for regulated financial institutions' trading environments, combines hybrid consensus with a privacy-preserving cryptographic approach. A hybridized consensus process for efficient transaction finality, zero-knowledge proof enabled atomic settlements for instant delivery vs. payment while protecting commercial secrecy, and regulator-accessible smart contracts for real-time compliance checks are used in the PACT algorithm PACT found a 20% reduction in consensus finality time, 53% reduction in proof verification time, 56% improvement in smart contract vulnerability, and 42% improvement in auditability index on a permissioned blockchain with hardware-accelerated smart contracts. The study indicated 35.6% lower throughput and 41.7% lower Tx volume over 10 validators. Latency over 10 validators is 24% lower and Tx volume is 23.2% lower than existing research models. Blockchain improves securities infrastructure speed, reliability, and transparency without affecting compliance, according to studies.
Antoine Bak, Guilhem Jazeron, Pierre Galissant, LĂŠo Perrin
In recent years, many hash functions have been introduced to satisfy the pressing need of some zero-knowledge protocols for such primitives allowing a low degree verification of their round function when arithmetized over a large field.While this can be achieved by restricting their sub-components to low-degree functions (and their inverse), the newest primitives in this category also leverage the intricacies of some proof systems to use âSplit-and-Lookupâ non-linear functions that essentially apply a small S-box in parallel over the binary representation of a field element.Such components excel at hindering attacks relying on polynomial system solving, but they offer poor security against statistical attacks. On the other hand, low degree monomials offer the opposite guarantees, being strong against statistical attacks. Several primitives have recently been proposed that combine such components in different ways in order to get the best from both.In this paper, we target such primitives by relying on the low degree components to allow a low-cost polynomial solving step. The weakness of Split-and-Lookups against linear attacks is used to simplify these systems, and their weakness against differential attacks is then used to propagate across many rounds the differential patterns obtained during polynomial solving. We instantiate this general approach by attacking round-reduced Monolith, and providing a distinguisher on full-round Skyscraper. These result then shed some light on how to best combine the different types of components to achieve the highest security.
The exponential growth of the electric vehicle (EV) industry, driven by decarbonization goals and energy transition policies, has intensified the need for sustainable and transparent supply chains. Lithium-ion batteries (LIBs), the cornerstone of EVs, pose complex life cycle challenges related to ethical sourcing, environmental degradation, traceability gaps, and inefficient end-of-life (EOL) management. Addressing these multifaceted issues requires an integrated technological approach. This study proposes a unified framework leveraging Digital Product Passports (DPPs) and blockchain technology to enable real-time, tamper-proof tracking of battery materials, components, and performance metrics throughout their lifecycle.The paper further integrates machine learning, with a focus on reinforcement learning (RL), to optimize logistics and predictive maintenance based on dynamic supply chain data. To ensure privacy and regulatory compliance in data sharing, the framework incorporates zkSNARKsâa zero-knowledge proof system that preserves confidentiality while maintaining verifiability across distributed networks. This triadic approach promotes lifecycle transparency, supports circular economy goals through efficient material reuse and recycling, and reduces the total cost of ownership (TCO) for EV stakeholders.The proposed solution addresses critical industry challengesâsuch as counterfeit components, low recycling efficiency, and supply chain opacityâwhile offering scalable applications in adjacent sectors like consumer electronics and renewable energy. The integration of DPPs, blockchain, and AI-based optimization establishes a resilient, interoperable infrastructure that enables enhanced sourcing, sustainability, and collaborative innovation in the evolving EV ecosystem.
Ensuring the integrity of business processes without disclosing confidential business information is a major challenge in inter-organizational processes. This paper introduces a zero-knowledge proof (ZKP)-based approach for the verifiable execution of business processes while preserving confidentiality. We integrate ZK virtual machines (zkVMs) into business process management engines through a comprehensive system architecture and a prototypical implementation. Our approach supports chained verifiable computations through proof compositions. On the example of product carbon footprinting, we model sequential footprinting activities and demonstrate how organizations can prove and verify the integrity of verifiable processes without exposing sensitive information. We assess different ZKP proving variants within process models for their efficiency in proving and verifying, and discuss the practical integration of ZKPs throughout the Business Process Management (BPM) lifecycle. Our experiment-driven evaluation demonstrates the automation of process verification under given confidentiality constraints.
Rui Han, Bin Yuan, Weizhong Qiang, Deqing Zou ¡ 5 authors
The widespread use of IoT devices in the accommodation and hospitality sectors has created demand for temporary device-permission sharing and transfer. Prior work has largely focused on security issues in device permission sharing, with far less attention devoted to device permission transfer. However, inappropriate access control management during device permission transfer can also lead to violations of the users' expectations of control over their devices. For example, a malicious host retaining or regaining access to a camera after its permission has been transferred to a tenant. In this paper, we present the first systematic study on understanding and enhancing the security of device permission transfer in IoT leasing. To this end, we propose Forseti, a new authorization framework that leverages zero-knowledge proof and a decentralized ledger to ensure that the rights of both hosts and tenants are not violated. Our evaluation demonstrates that Forseti is effective, efficient, scalable, and compatible with existing IoT platforms.
Federated Learning (FL) offers a promising paradigm for privacy-preserving collaborative training, yet it remains highly vulnerable to adversarial behaviors, client unreliability, and challenges associated with non-independent and identically distributed (non-IID) data. Existing secure aggregation techniques, while preserving confidentiality, fail to guarantee the integrity and trustworthiness of model updates, leaving FL deployments exposed to poisoning and consistency attacks. This work introduces FL-SMPC++, a robust and privacy-preserving FL framework designed to address these challenges. The primary objective is to develop a scalable solution that ensures verifiable, privacy-preserving aggregation while mitigating malicious client behaviors, dropouts, and data heterogeneity. Our approach integrates Secure Multi-Party Computation (SMPC), Pedersen commitments, and zero-knowledge proofs (ZKPs) to cryptographically bind clients' submitted updates to their validation outcomes without revealing private data. We propose a dynamic client selection strategy based on shared validation performance, a dropout-tolerant threshold aggregation protocol, and a warm-up initialization phase to counteract non-IID distributions. Comprehensive experiments on MNIST, CIFAR-10, FEMNIST, and UCI Heart Disease show that FL-SMPC++ consistently outperforms FedAvg, FedProx, and FedNova. For example, under a label-flipping attack with 30% malicious clients on CIFAR-10 (non-IID), FL-SMPC++ achieves 78.9% accuracy compared to 67.4% for FedAvg, representing an absolute gain of 11.5%. Across datasets, the framework limits accuracy degradation to 6â8% under attack, while baselines suffer 13â20% losses. These results demonstrate that FL-SMPC++ achieves strong cryptographic privacy guarantees together with empirically validated resilience and convergence, offering a scalable and practical blueprint for trustworthy FL in adversarial and resource-constrained environments. ⢠A novel FL framework combines SMPC, commitments, and zero-knowledge proofs. ⢠Ensures submitted model updates match validated ones without revealing them. ⢠Uses dynamic validation for secure and fair client selection. ⢠Tolerates client dropouts using a threshold-based aggregation mechanism. ⢠Outperforms baseline FL methods under adversarial and non-IID conditions.
The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-QuĂŠbec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, DemandâSupply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing leadâacid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations.
Fintech modernization is a ground-up shift from traditional batch-processing infrastructure to event-driven real-time architectures that redefine financial service delivery and social mechanisms of trust. Modern financial institutions draw on advanced stream processing technologies, API-first integration, and distributed computing to support transaction throughput rates in millions of operations per second with sub-millisecond latencies for key financial transactions. Occasion-driven architectures (also known as event-driven architecture) provide instantaneous affirmation of transactions, real-time detection of fraud, and clear audit trails through immutable event recording structures that ensure end-to-end transaction traceability for regulatory purposes. Mobile-first design patterns and modern web-based packages boost access to finance for the underprivileged through offline-enabled interfaces that function across diverse device specifications and network connectivity eventualities. Advanced cryptographic algorithms, which include homomorphic encryption and zero-knowledge proofs, facilitate privacy-enhancing analytics that reconcile customized financial offerings in opposition to peopleâs privacy protection. Regulatory technology embedding using compliance-by-design architectures in regulatory technology help automate policy application and reporting while advanced trust protocols using biometric authentication, behavior analysis, and machine learning algorithms prevent fraud while ensuring frictionless user experiences. The intersection of distributed architectures, privacy-retaining technologies, and inclusive design styles generates financial structures that cater to various populations even as adhering to demanding safety and regulatory compliance in diverse jurisdictions.
Transaction-Ordering Dependence (TOD) is a potential vulnerability of blockchain-based smart contracts, which allows malicious actors to exploit the order of transactions to obtain financial profit through front-running and back-running strategies.The purpose of this paper is to present ZkDelay, a new framework that jointly uses commitment schemes and Verifiable Delay Functions (VDFs) to counter TOD in decentralized applications.ZkDelay introduces a two-step transaction scheme: a user makes a cryptographic commitment to a transaction without announcing its purpose, and then, upon completing a verifiable delay with a VDF, the intended transaction can be revealed and carried out.This temporal discontinuity, combined with cryptographic acknowledgments, prevents adversaries from interfering with actionable knowledge in real-time, thereby eliminating any orderingbased attack possibilities.Moreover, ZkDelay is transparent and trustless, as it can be used to verify both commitments and delay execution through zero-knowledge proofs, without leaking sensitive data.Additional sections dedicated to rigorous security analysis and performance analysis in Ethereum-like environments are provided in the paper, demonstrating that ZkDelay incurs only a low amount of computational overhead and that it exponentially improves resistance to TOD attacks.The solution can be deployed in existing smart contract systems and adapted to DeFi protocols, order-sensitive auctions, and other mechanisms.ZkDelay addresses the challenge of integrating privacy-preserving mechanisms with the fairness of execution by providing a scalable and practical solution to one of the most prevalent security issues in smart contract environments.
Zero-knowledge rollups rely on provers to generate multi-step state transition proofs under strict finality and availability constraints. These steps require expensive hardware (e.g., GPUs), and finality is reached only once all stages complete and results are posted on-chain. As rollups scale, staying economically viable becomes increasingly difficult due to rising throughput, fast finality demands, volatile gas prices, and dynamic resource needs. We base our study on Halo2-based proving systems and identify transactions per second (TPS), average gas usage, and finality time as key cost drivers. To address this, we propose a parametric cost model that captures rollup-specific constraints and ensures provers can keep up with incoming transaction load. We formulate this model as a constraint system and solve it using the Z3 SMT solver to find cost-optimal configurations. To validate our approach, we implement a simulator that detects lag and estimates operational costs. Our method shows a potential cost reduction of up to 70\%.
Digital payments have grown exponentially but face risks such as fraud, account takeover, and unauthorized transactions. This paper explores how blockchain technology, with its decentralized ledger, cryptographic integrity, and smart contracts, can secure digital payments and prevent fraud. We propose a permissioned blockchain architecture for payment systems, integrating identity management, escrow-based smart contracts, and audit-ready transaction logs. Illustrative simulations compare fraud-risk indices, transaction confirmation time, and per-transaction cost across traditional payment gateways and blockchain systems. The results indicate that blockchain can reduce fraud exposure while maintaining near real-time settlement. Challenges such as scalability, privacy, and regulatory compliance are also discussed. This study highlights blockchain?s potential as a preventive, secure mechanism for digital payments and sets the stage for future research integrating zero-knowledge proofs and federated learning.
Mohamed Abdessamed Rezazi, Mouhamed Amine Bouchiha, A. Bendada, Yacine Ghamri-Doudane
Roaming settlement in 5G and beyond networks demands secure, efficient, and trustworthy mechanisms for billing reconciliation between mobile operators. While blockchain promises decentralization and auditability, existing solutions suffer from critical limitations-namely, data privacy risks, assumptions of mutual trust, and scalability bottlenecks. To address these challenges, we present B5GRoam, a novel on-chain and zero-trust framework for secure, privacy-preserving, and scalable roaming settlements. B5GRoam introduces a cryptographically verifiable call detail record (CDR) submission protocol, enabling smart contracts to authenticate usage claims without exposing sensitive data. To preserve privacy, we integrate non-interactive zero-knowledge proofs (zkSNARKs) that allow on-chain verification of roaming activity without revealing user or network details. To meet the high-throughput demands of 5G environments, B5GRoam leverages Layer 2 zk-Rollups, significantly reducing gas costs while maintaining the security guarantees of Layer 1. Experimental results demonstrate a throughput of over 7,200 tx/s with strong privacy and substantial cost savings. By eliminating intermediaries and enhancing verifiability, B5GRoam offers a practical and secure foundation for decentralized roaming in future mobile networks.
Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspectsâsuch as communication or trustâbut fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.
The article presents a comprehensive overview of zero-knowledge proof (ZKP) protocols as a fundamental concept of modern cryptography. The historical background of their emergence and the main properties ensuring reliability and confidentiality, i.e., completeness, soundness, and zero-knowledge â are considered. A classification of protocols into interactive and non-interactive ones is provided, with a special focus on modern solutions such as the zk-SNARK and the zk-STARK. The mathematical foundations of ZKPs are described in detail, including discrete logarithm proofs, the use of homomorphic encryption, polynomial commitments, hashing, and elliptic curves. Practical application areas are analyzed, including cryptocurrencies (Zcash, Ethereum), authentication systems, digital identity, and electronic voting. The advantages of using ZKPs are shown, such as enhanced privacy, reduced need for trusted intermediaries, and strengthened security. At the same time, key challenges are outlined, including scalability, implementation complexity, the problem of trusted setup, and potential vulnerability to quantum computing. It is concluded that zero-knowledge proof protocols are a powerful tool for ensuring confidentiality and reliability of digital systems, while further research is aimed at creating more efficient and quantum-resistant solutions.
ChipmunkRing, a practical post-quantum ring signature construction tailored for blockchain environments. Building on our Chipmunk lattice-based cryptographic framework, this implementation delivers compact digital signatures ranging from 20.5 to 279.7KB, with rapid signing operations completing in 1.1-15.1ms and efficient validation processes requiring only 0.4-4.5ms for participant groups of 2-64 members. The cornerstone of our approach is Acorn Verification-a streamlined zero-knowledge protocol that supersedes the classical Fiat-Shamir methodology. This innovation enables linear O(n) authentication complexity using concise 96-byte cryptographic proofs per participant, yielding a remarkable 17.7x performance enhancement for 32-member rings when compared to conventional techniques. Our work includes rigorous mathematical security demonstrations confirming 112-bit post-quantum protection (NIST Level 1), extensive computational benchmarking, and comprehensive support for both standard anonymity sets and collaborative threshold constructions with flexible participation requirements.
Supply chain finance, a critical tool for industrial chain coordination in the digital economy, faces challenges such as centralized identity authentication, data silos, and privacy risks, with traditional models constrained by single-point failures and inefficient data sharing.While blockchain's decentralized and tamper-proof features offer a solution, existing approaches often lack comprehensiveness.To address this, this study proposes two integrated solutions: first, the ZK-SCFI scheme, which leverages Merkle trees for identity storage, Paillier homomorphic encryption for pseudo-identity generation, and zero-knowledge proofs for privacy-preserving verification, effectively avoiding centralized risks and ensuring transaction non-associability; second, the TRU-SABE framework, combining blockchain and IPFS to enhance ciphertext-policy attribute-based encryption (CP-ABE) with keyword search, user revocation, outsourced decryption, and malicious user tracking, addressing traditional limitations like high computational costs and scalability issues.Experiments demonstrate TRU-SABE's superior efficiency, with user-side decryption overhead reduced to 3TE and storage advantages from factor group structures for attribute keys and ciphertexts, while the hybrid architecture alleviates data silos and storage pressure.A prototype system built on Fisco Bcos consortium blockchain, Spring Boot backend, and Vue.js frontend validates core functionalities, including encrypted data sharing and smart contract-based traceability.This work provides a holistic privacy-preserving solution for supply chain finance, with future directions focusing on balancing privacy with regulatory compliance, optimizing multi-authority key management, and expanding system capabilities to enhance security and efficiency.
In the context of a growing shift towards DeFi, the challenge of user identity verification while maintaining privacy remains a daunting task. This paper proposes a model for identity verification based on Zero-Knowledge Proofs (ZKPs) tailored for distributed financial contexts. The model implemented uses cryptographic methods to verify identity claims while maintaining the confidentiality of the personal information, achieving a delicate equilibrium between privacy and financial compliance. The privacy-compliant framework increases security while satisfying legal compliance by removing the need to trust a single party and decreasing exposure of personal information and data. The proposed model improves privacy, verification speed, and fraud resistance compared to conventional and baseline systems.
Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Multiplication (MSM). However, we find that a systematic characterization of execution bottlenecks in ZKPs, as well as their scalability on modern GPU architectures, is missing in the literature. This paper presents ZKProphet, a comprehensive performance study of Zero-Knowledge Proofs on GPUs. Following massive speedups of MSM, we find that ZKPs are bottlenecked by kernels like Number-Theoretic Transform (NTT), as they account for up to 90% of the proof generation latency on GPUs when paired with optimized MSM implementations. Available NTT implementations under-utilize GPU compute resources and often do not employ architectural features like asynchronous compute and memory operations. We observe that the arithmetic operations underlying ZKPs execute exclusively on the GPU's 32-bit integer pipeline and exhibit limited instruction-level parallelism due to data dependencies. Their performance is thus limited by the available integer compute units. While one way to scale the performance of ZKPs is adding more compute units, we discuss how runtime parameter tuning for optimizations like precomputed inputs and alternative data representations can extract additional speedup. With this work, we provide the ZKP community a roadmap to scale performance on GPUs and construct definitive GPU-accelerated ZKPs for their application requirements and available hardware resources.