In this paper, we show for the first time it is practical to privately delegate proof generation of zkSNARKs to a single server for computations of up to 2^20 R1CS constraints. We achieve this by computing zkSNARK proof generation over homomorphic ciphertexts, an approach we call blind zkSNARKs. We formalize the concept of blind proofs, analyze their cryptographic properties and show that the resulting blind zkSNARKs remain sound when compiled using BCS compilation. Our work follows the framework proposed by Garg et al. (Crypto'24) and improves the instantiation presented by Aranha et al. (Asiacrypt'24), which implements only the FRI subprotocol. By delegating proof generation, we are able to reduce client computation time from 10 minutes to mere seconds, while server computation time remains limited to 20 minutes. We also propose a practical construction for vCOED supporting constraint sizes four orders of magnitude larger than the current state-of-the-art verifiable FHE-based approaches. These results are achieved by optimizing Fractal for the GBFV homomorphic encryption scheme, including a novel method for making homomorphic NTT evaluation packing-friendly by computing it in two dimensions. Furthermore, we make the proofs publicly verifiable by appending a zero-knowledge Proof of Decryption (PoD). We propose a new construction for PoDs optimized for low proof generation time, exploiting modulus and ring switching in GBFV and using the Schwartz-Zippel lemma for proof batching; these techniques might be of independent interest. Finally, we implement the latter protocol in C and report on execution time and proof sizes.
The industrial internet of things (IIoT) expanded fast as physical devices and systems were connected to the internet. However, this interconnectedness made IIoT systems vulnerable to hackers. Intrusion detection systems (IDSs) were put in place to detect and prevent such assaults. Nonetheless, attackers might circumvent IDSs by forging identities or interfering with recorded data. The article intended to improve IIoT security by achieving system confidentiality, integrity, availability, scalability, performance, and security. For IIoT security, the article developed a secure federated learning access control framework (SecureFLACF) linked with a blockchain-based IDS. SecureFLACF used blockchain to secure data collected by IDS, AES-256 encryption to secure stored data, zero-knowledge proof (ZKP) to validate user identities and manage data access, and a federated learning access control framework (FLACF) to train a machine learning model for intrusion detection. SecureFLACF developed as a viable solution for improving IIoT security, providing strong assurances for IDS data and access control using blockchain’s tamper-proof structure and AES-256 encryption. Furthermore, FLACF’s design allows private machine learning model training, ensuring data privacy as well as model fidelity. The framework’s usefulness was highlighted by its application in real-world circumstances, making it a cost-effective option for organisations of all sizes. This method not only strengthened IIoT systems against a wide range of cyber threats, but also stressed their dependability as a safeguard. SecureFLACF exhibited considerable promise for improving IIoT security across several dimensions by encapsulating practicability, cost-effectiveness, and dependability.
Existing regulatory frameworks for decentralized anonymous payments help combat illicit activities such as money laundering and terrorism financing. However, With the development of the European Union's General Data Protection Regulation (GDPR) and data sovereignty, Existing frameworks struggle to balance privacy with regulatory compliance, often compromising user autonomy and data sovereignty. To address this, we propose the Linkable Distributed Regulatory Tag (LDRT) scheme, which enables traceability without altering transaction structure by leveraging Paillier homomorphic encryption and secret sharing. Building upon this, we introduce Decentralized Anonymous Payment with Data Sovereignty(DAPDS), the first regulatory framework for decentralized anonymous payments that complies with data sovereignty. DAPDS supports both UTXO and account-based models and employs a distributed regulator group with incentives to prevent dishonest behavior. We define and analyze the security properties of both LDRT and DAPDS, proving anonymity, linkability, traceability, collusion resistance, data sovereignty, distributed regulation, and fairness of incentive mechanism. Our work provides a valuable reference for future regulatory framework paradigms for anonymous privacy-preserving traceable blockchain. Experimental results show that DAPDS only incurs an additional 34ms of time cost over ETH in transacation phase and an average of only 2.66s per tracing request in tracing phase.
Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae Min Lee, Dong‐Seong Kim
The growing complexity of distributed industrial IoT systems heightens cybersecurity risks, exposing the limitations of centralized ML-based intrusion detection. Federated Learning (FL) enables decentralized, privacy-preserving model training but remains susceptible to adversarial threats and system-level failures. This study introduces PureChain, a decentralized ledger using a proof-of-authority and association (PoA 2 ) consensus mechanism to enhance FL-based IDS security. The study offers insight into the mathematical model of the PureChain-enhanced FL, which integrates blockchain-inspired consensus protocols for collaborative intrusion detection across organizations, ensuring data privacy while providing tamper-proof logs and automated responses through smart contracts. It incorporates dynamic fault tolerance, poisoning resistance, and privacy preservation with FL, enhancing security and performance in decentralized systems. Experimentation with varying client subsets demonstrates its adaptability with a TPS range of 312 . 5 − 1178 . 3 and a low latency range of 0 . 0008484 − 0 . 0032 . The framework ensures comprehensive security, reliability, and privacy, providing a scalable solution for decentralized, secure systems.
Advances in differential equations and control theory are reshaping how secure, efficient medical data-exchange systems are designed. In parallel, blockchain offers decentralized trust, cryptographic integrity, and auditable access control for healthcare networks. Yet the choice of storage and transmission architecture strongly affects scalability, latency, privacy, and cost. This work investigates how mathematical modeling via differential equations and modern control processes can be coupled with blockchain to strengthen security and interoperability across distributed healthcare systems. We comparatively examine three deployment models: (1) on-chain storage, (2) off-chain, cloud-backed storage with blockchain access control, and (3) local institutional storage integrated with federated learning. On-chain designs maximize transparency and tamper-resistance but incur substantial computation and storage overhead. Off-chain approaches improve scalability while retaining verifiable control through the ledger. Local storage with federated learning safeguards patient privacy by keeping raw data within institutions and sharing only encrypted updates or proofs on chain. Persistent challenges include storage bloat, network delays, heterogeneous regulations, and evolving attack surfaces. To address these issues, we outline optimization strategies grounded in system dynamics stability analysis, resource allocation, and control-oriented tuning to balance throughput, privacy, and reliability. The study synthesizes theoretical insights with implementation considerations, offering a unified perspective on building resilient, performant, and privacy-preserving medical data-exchange frameworks that leverage blockchain under mathematically principled control.
The rapid progress of large-scale models, including foundational and generative, brings to the forefront the tension between data-driven innovation and core privacy concerns. Such contracts as the GDPR and the undue privacy threats of data aggregation make centralized training approaches less desirable. To analyze the data’s distributed characteristics and their application to FLO, we investigate the role of federation analytics in a plausible paradigm that shunts data. In this paper, we present a new federated learning (FL) framework enhanced with cutting-edge privacy technologies (PET) such as Differential privacy for user-level formal guarantees of confidentiality, and strengthened secure Multi-Party Computation (SMPC), which guards the model updates. This paper studies more recent approaches to resolving the principal challenges of FL: statistical heterogeneity, communication bottlenecks, and vulnerability to adversarial attacks. We greatly appreciate what this new method portends, especially for training large language models (LLMs) and the more delicate areas of healthcare and finance. By evaluating certain existing limitations, such as the complexities of federated fine- tuning and model fairness, it is clear that an architecture with exemplary performance in FL serves as a model for scalable, secure, and privacy cop.
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger's efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments.
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.
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 tools—suchas masking, pseudonymization, and data perturbation—tomitigate 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.
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 6–8% under attack, while baselines suffer 13–20% 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.
Zero Trust is now the de facto standard to secure cloud-native, distributed, and AI-driven enterprise infrastructures. It's not only crucial to address human identities but also to secure non-human entities such as APIs, software agents, RPA bots, and smart city workloads. As hybrid infrastructures become the new normal and agentic AI systems (e.g., self-driving cars) grow more autonomous, identity remains the most stable and trustworthy security control plane. This document proposes an intent-aware Zero Trust Identity Architecture designed to consolidate governance, authentication, and access control for human and non-human entities. The architecture consists of decentralized identity provisioning, policy-as-code enforcement, real-time telemetry ingestion, trust scoring, and AI-powered intent detection to provide inputs for continuous verification and least privilege enforcement. Compliant with standards such as NIST SP 800-207, NIST SP 800-63, CISA Zero Trust Maturity Model, and DoD's Zero Trust Strategy, the architecture also aligns with industry developments from Microsoft Entra ID, AWS IAM Identity Center, Google BeyondCorp, SPIFFE/SPIRE, and W3C DIDs. The whitepaper explores use cases in healthcare, finance, retail, and industrial IoT spaces that are struggling with unique challenges like OT/IT convergence, multi-user devices, and governance of sensitive data access. High-profile attacks such as SolarWinds, MOVEit, and Log4Shell are broken down to highlight weaknesses in legacy IAM architectures and underscore the need for intent-based security. By intersecting behavior, purpose, and identity, this architecture remakes trust in hybrid, edge, and cloud-native settings with a conclusion of actionable paths of mitigation and a vision for intent-based Zero Trust governance
The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions.
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 aspects—such as communication or trust—but 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.
With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline.
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.
Traceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.
Decentralized Federated Learning (DFL) enables collaborative model training without a central server, but it remains vulnerable to privacy leakage because shared model updates can expose sensitive information through inversion, reconstruction, and membership inference attacks. Differential Privacy (DP) provides formal safeguards, yet existing DP-enabled DFL methods operate as black-boxes that cannot track cumulative noise added across clients and rounds, forcing each participant to inject worst-case perturbations that severely degrade accuracy. We propose PrivateDFL, a new explainable and privacy-preserving framework that addresses this gap by combining a HyperDimensional Computing (HD) model with a transparent DP noise accountant tailored to decentralized learning. HD offers structured, noise-tolerant high-dimensional representations, while the accountant explicitly tracks cumulative perturbations so each client adds only the minimal incremental noise required to satisfy its (epsilon, delta) budget. This yields significantly tighter and more interpretable privacy-utility tradeoffs than prior DP-DFL approaches. Experiments on MNIST (image), ISOLET (speech), and UCI-HAR (wearable sensor) show that PrivateDFL consistently surpasses centralized DP-SGD and Renyi-DP Transformer and deep learning baselines under both IID and non-IID partitions, improving accuracy by up to 24.4% on MNIST, over 80% on ISOLET, and 14.7% on UCI-HAR, while reducing inference latency by up to 76 times and energy consumption by up to 36 times. These results position PrivateDFL as an efficient and trustworthy solution for privacy-sensitive pattern recognition applications such as healthcare, finance, human-activity monitoring, and industrial sensing. Future work will extend the accountant to adversarial participation, heterogeneous privacy budgets, and dynamic topologies.
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a central server while keeping in- and output layers on the client-side. This setup enables SL to leverage server computation capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data. However, the distributed nature enables malicious clients to manipulate the training process. By sending poisoned intermediate gradients, they can inject backdoors into the shared DNN. Existing defenses are limited by often focusing on server-side protection and introducing additional overhead for the server. A significant challenge for client-side defenses is enforcing malicious clients to correctly execute the defense algorithm. We present ZORRO, a private, verifiable, and robust SL defense scheme. Through our novel design and application of interactive zero-knowledge proofs (ZKPs), clients prove their correct execution of a client-located defense algorithm, resulting in proofs of computational integrity attesting to the benign nature of locally trained DNN portions. Leveraging the frequency representation of model partitions enables ZORRO to conduct an in-depth inspection of the locally trained models in an untrusted environment, ensuring that each client forwards a benign checkpoint to its succeeding client. In our extensive evaluation, covering different model architectures as well as various attack strategies and data scenarios, we show ZORRO's effectiveness, as it reduces the attack success rate to less than 6\% while causing even for models storing \numprint{1000000} parameters on the client-side an overhead of less than 10 seconds.
The advancement in smart mobility communication technology allows intelligent vehicles to interconnect and communicate with each other to improve traffic safety and efficiency. However, the highly dynamic nature of the smart mobility network and vehicle behaviour creates the requirement for effective authentication systems to establish secure and reliable communication between vehicles. The implementation of a reputation system has been proposed to establish trust among untrusted vehicles, where the reliability of a propagated message is assessed based on the reputation of the sender vehicle. Thus, preventing malicious vehicles from potentially broadcasting misleading messages that can cause accidents or disrupt the network. This paper proposes a novel decentralised and dynamic reputation management and computation model based on a consortium blockchain and a multi-signature smart contract. The implementation of blockchain and a smart contract provides a secure and transparent framework for registering vehicles, submitting events, voting feedback, evaluating reputation, and blocking malicious vehicles. To demonstrate the feasibility of the proposed model, we conducted security and performance analyses. The results demonstrate how our model can provide resistance against various attacks, such as data tampering, message forging, self-promotion, vote duplication, bad-mouth, onoff, time-dependent, and collusion attacks.
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Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Abstract Verifiable encryption enables the decryption to be taken on properly generated ciphertexts, by making the encryptor provide a zero-knowledge proof. To meet the quantum-safe application requirements, such as key escrow, Lyubashevsky et al. proposed a one-shot verifiable encryption (LN17 scheme) based on the hardness of lattice problems. In their scheme, the FSwA-type zero-knowledge proof was obtained using rejection sampling on a discrete Gaussian distribution. In this paper, we present a construction of verifiable encryption that utilizes rejection sampling on bimodal Gaussian to get the associated zero-knowledge proof. Our new construction, while exhibiting a weaker soundness property than LN17 scheme, benefits from a smaller proof size, leading to a reduced size of the verifiable ciphertext. As for the weaker soundness property, it supports some applications such as key escrow where honestly generated verifiable ciphertexts are more useful to be decrypted out in the hope of doing some further computation tasks. We provide the efficiency comparison of the new construction by instantiating it with several sets of concrete parameters.
Nicolò Romandini, Carlo Mazzocca, Kai Otsuki, Rebecca Montanari
Blockchain and smart contracts have garnered significant interest in recent years as the foundation of a decentralized, trustless digital ecosystem, thereby eliminating the need for traditional centralized authorities. Despite their central role in powering Web3, their complexity still presents significant barriers for non-expert users. To bridge this gap, Artificial Intelligence (AI)-based agents have emerged as valuable tools for interacting with blockchain environments, supporting a range of tasks, from analyzing on-chain data and optimizing transaction strategies to detecting vulnerabilities within smart contracts. While interest in applying AI to blockchain is growing, the literature still lacks a comprehensive survey that focuses specifically on the intersection with AI agents. Most of the related work only provides general considerations, without focusing on any specific domain. This paper addresses this gap by presenting the first Systematization of Knowledge dedicated to AI-driven systems for blockchain, with a special focus on their security and privacy dimensions, shedding light on their applications, limitations, and future research directions.
Modern vehicles accumulate fragmented lifecycle records across OEMs, owners, and service centers that are difficult to verify and prone to fraud. We propose VehiclePassport, a GAIA-X-aligned digital passport anchored on blockchain with zero-knowledge proofs (ZKPs) for privacy-preserving verification. VehiclePassport immutably commits to manufacturing, telemetry, and service events while enabling selective disclosure via short-lived JWTs and Groth16 proofs. Our open-source reference stack anchors hashes on Polygon zkEVM at <$0.02 per event, validates proofs in <10 ms, and scales to millions of vehicles. This architecture eliminates paper-based KYC, ensures GDPR-compliant traceability, and establishes a trustless foundation for insurance, resale, and regulatory applications in global mobility data markets.