Sample alignment performs a crucial role in vertical federated learning, aiming to identify shared user samples among multiple parties without exposing their private identifier data. However, most existing alignment protocols are designed for two-party scenarios, while those developed for multi-party settings suffer from limited anti-collusion capability and inefficient verification mechanisms. To address these issues, we propose an efficient and secure protocol for sample alignment in multi-party vertical federated learning (MESA). The protocol leverages a threshold oblivious pseudo-random function (T-OPRF) combined with a distributed key generation scheme to defend against collusion attacks. Moreover, an oblivious key–value store encoding (OKVS) mechanism is introduced to enable secure and efficient key–value mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.
Dileep Kumar Murala, Shadab Ahmad, V. A. Sankar Ponnapalli, Veera Ankalu Vuyyuru · 5 authors
Wireless sensor networks (WSNs) will be necessary for the next generation of Internet of Things (IoT) apps. They make it possible to use smart and long-lasting sensors and smart automation in healthcare, Industry 4.0, and critical infrastructure. But security is particularly hard since they have built-in flaws, not enough computer power, not enough energy, and a significant danger of insider threats. Standard encryption methods aren't enough, and in situations where resources are restricted, heavier blockchain or machine learning solutions aren't always possible. This study presents ChainShieldML, a lightweight hybrid security architecture that combines Blockchain (BC) and machine learning (ML) to provide decentralised, adaptive, and resource-efficient protection for wireless sensor networks (WSNs). The idea is based on a two-pronged defence strategy. The Blockchain Prevention Module's permissionless blockchain architecture for base stations and cluster heads makes it possible to verify identities, maintain trust in a decentralised way, and keep node interactions unchangeable. Smart contracts made in solidity and connected to the Ethereum ecosystem make it possible to safely register nodes and keep an eye on what they do. The VBFT consensus algorithm makes it possible to quickly validate without using as much computing power as most proof of work methods. The machine learning detection module uses the lightweight gradient boosting method (LightGBM) to find and rank dangerous nodes in real time. LightGBM is the best machine learning classifier when looking at things like recall, F1-score, Matthews correlation coefficient, training cost, and inference latency. ChainShieldML dramatically improves the detection of insider attacks, builds trust, and protects data while using very little energy and having very little communication delay, as shown in tests. For Wireless sensor networks (WSNs) to keep working, all of these things are very important. ChainShieldML is a novel solution to keep IoT devices safe. It uses blockchain's decentralised trust and ML's adaptive intelligence to make a defence system for next-generation wireless sensor networks that can grow, is strong, and is ready for the future.
Chi Zhang, Fenhua Bai, Xiaohui Zhang, Jinhua Wan · 6 authors
As a middleware technology in distributed computer systems, blockchain systems represent a paradigm for achieving node interconnectivity. Despite this, technical differences between various blockchain networks have led to the emergence of a phenomenon known as multi-chain, where inter-chain communication has become a trust barrier. Cross-chain technology is a powerful tool that allows data to flow between different blockchain networks, breaking down data barriers and enabling seamless data transfer. However, cross-chain identification may lead to potential risks such as the exposure of private information and data loss or tampering. In this brief, we propose Universal Cross-Chain Permissioned Blockchain (UCCPB) architecture, which connects single permissioned chains into a multi-chain system. Based on this, the Cross-Chain Anonymous Identity Authentication (CCAIA) model is proposed, which implements privacy-preserving chain identity registration and verification through zero-knowledge proof without a trusted setup. Furthermore, we propose the Proof of Cross-Chain Invocation (PoCI) mechanism of UCCPB, which consists of a node election and consensus on the invocation result. This mechanism ensures the correctness of the cross-chain invocation results and incentivizes nodes to participate in UCCPB. Our experiments show that the proposed UCCPB achieves a balance between performance and privacy while improving the security of cross-chain invocations.
Yu Gao, Carlo Campajola, Nicolò Vallarano, Andreia Sofia Teixeira · 5 authors
IOTA is a distributed ledger technology that relies on a peer-to-peer (P2P) network for communications. Recently an auto-peering algorithm was proposed to build connections among IOTA peers according to their “Mana" endowment, which is an IOTA internal reputation system. This paper’s goal is to detect potential vulnerabilities and evaluate the resilience of the P2P network generated using IOTA auto-peering algorithm against eclipse attacks. In order to do so, we interpret IOTA’s auto-peering algorithm as a random network formation model and employ different network metrics to identify cost-efficient partitions of the network. As a result, we present a potential strategy that an attacker can use to eclipse a significant part of the network, providing estimates of costs and potential damage caused by the attack. On the side, we provide an analysis of the properties of IOTA auto-peering network ensemble, as an interesting class of homophile random networks in between 1D lattices and regular Poisson graphs.
The paper explores the possibility of expanding the use of end-to-end encryption protocols based on the Double Ratchet algorithm in applications with low trust in the server, particularly in turn-based games and strategic interactions. The relevance of the research is due to the growing need for secure communication in cyberattacks, especially during military operations. The field of end-to-end encryption requires the study of additional applications beyond the usual ones, such as encrypted communication in text messengers. The developed implementation of the protocol can be safely used in any applications that aim to implement end-to-end encryption and satisfy the criterion of session ephemerality (in cases where secrets are stored outside a secure environment). The implemented server supports ephemeral sessions, which guarantee minimal risks of information compromise, and uses digital signatures (EdDSA) for user authentication. Logical routing of requests ensures efficient message transmission in secure scenarios. The choice of the classic game of checkers as an example allowed the authors to effectively demonstrate the advantages of end-to-end encryption and the capabilities of the implemented protocol. All cryptographic operations, including key generation, encryption and decryption of messages, are successfully performed on client devices. It is important to improve error handling mechanisms and optimize the operation of WebAssembly. An interesting area of further research is the creation of zero-knowledge proof mechanisms to prevent Man-In-The-Middle attacks during the creation of a shared secret, optimizing integration with cryptographic hardware security modules (HSM), and exploring the scalability of the solution. The proposed approach can be used to solve real-world information security problems where trust in the data transmission channel is critically important. Thus, the work has created a comprehensive solution that includes a cryptographic protocol, a backend, and a web client, which demonstrates the viability of end-to-end encryption in browser environments and multiplayer games. The work can be used as a basis for further research and development in the field of security of communication systems and privacy in multiplayer games.
The proliferation of resource-constrained Internet of Things (IoT) devices poses formidable security challenges, rendering traditional centralized authentication mechanisms impractical. To address this issue, this paper proposes AMAKA (Anonymous Mutual Authentication and Key Agreement), a novel blockchain-fortified protocol specifically designed for IoT environments. AMAKA utilizes smart contracts to establish a robust framework for device lifecycle management, including registration, updates, and revocation, enabling fine-grained access control under the authority of the equipment manufacturer. The protocol's core synergizes Schnorr signatures with noninteractive zero-knowledge proofs to deliver strong guarantees of mutual authentication, user anonymity, unlinkability, perfect forward secrecy, and conditional traceability. We formally verify AMAKA's security against a wide range of attacks by employing the ProVerif tool under an active adversary model. Furthermore, a prototype deployed on a private Ethereum network demonstrates its practical viability, confirming low on-chain overhead, minimal storage demands, and high computational efficiency. Therefore, AMAKA provides a balanced, secure, and scalable authentication solution for large-scale IoT ecosystems.
Hang Liu, Ming Yang, Aotian Cai, Chenhao Wang · 5 authors
In recent years, with the increasing prevalence of online group chat applications, malicious information has been more easily disseminated on the internet. Asymmetric group message franking (AGMF) allows users to report received malicious messages to moderators, achieving content moderation in large-scale online end-to-end messaging systems. However, the state-of-the-art construction is built upon traditional public key cryptosystems, resulting in the complex certificate management problem. This paper systematically explores identity-based AGMF (IB-AGMF) to resolve this issue. Specifically, we first introduce a novel primitive called hash proof system-based anonymous identity-based key encapsulation mechanism supporting sigma protocol (HPS-AIB-KEMΣ) and present a practical construction based on DBDH assumption. After formalizing the concept and security notions of IB-AGMF, we propose the generic construction of IB-AGMF based on HPS-AIB-KEMΣand non-interactive zero knowledge proof system. Finally, we conduct comprehensive performance evaluations and comparisons to demonstrate the feasibility of IB-AGMF in group communication scenarios.
This paper presents a comparative analysis of three cryptographic approaches to securing fiber-optic data transmission: classical RSA/AES, Quantum Key Distribution (QKD) based on the E91 protocol, and Secure Dynamic Stream Encryption (SDSE). These are evaluated in terms of latency, key renewal frequency, computational load, energy efficiency, and resilience to quantum attacks. In addition to these core comparisons, we extend SDSE with a blockchain-based governance layer. This enhancement introduces auditable key management through distributed ledger technology (DLT), ensuring transparent tracking of key rotations, revocations, and policy enforcement without storing any secret material. The blockchain acts as a trust anchor for multi-node synchronization, particularly useful in federated or multi-domain environments. The combined SDSE + Blockchain framework provides a lightweight, quantum-resilient, and scalable encryption mechanism suitable for real-world deployment across conventional optical infrastructure.
Wencheng Chen, Jun Wang, Jeng‐Shyang Pan, R. Simon Sherratt · 5 authors
With the rapid expansion of sensor networks across domains such as environmental monitoring, industrial automation, and smart healthcare, ensuring secure and reliable data storage in resource-constrained environments has become a critical challenge. Traditional centralized storage systems struggle with data tampering, privacy leakage, and vulnerability to collusion among nodes. Blockchain technology, characterized by decentralization, immutability, and traceability, provides a promising foundation for trustworthy sensor data management. Among various consensus mechanisms, Delegated Proof of Stake (DPoS) has been recognized for its efficiency and low energy consumption, yet it faces two critical issues: limited incentives for ordinary sensor nodes to participate in voting and the risk of collusion that undermines fairness and stability. To overcome these limitations, this study proposes a blockchain-enabled sensor data storage framework incorporating a four-party evolutionary game model. The model explicitly captures the strategic interactions among cluster head nodes, ordinary sensor nodes, competing gateway nodes, and supervisory nodes, while integrating reputation evaluation, penalty enforcement, and supervisory oversight. Through evolutionary game analysis, the proposed framework reveals the stability conditions of node behaviors and identifies strategies that promote fair and secure consensus. Simulation results verify that the mechanism enhances node participation, suppresses collusion, accelerates consensus convergence, and achieves superior throughput and fault tolerance compared with existing schemes. This research provides theoretical insights and practical guidance for designing secure, efficient, and scalable blockchain-enabled sensor network data storage systems.
Vehicular ad-hoc networks (VANETs), considered a pivotal component of intelligent transportation systems (ITS), are susceptible to both established and emerging security vulnerabilities. However, existing authenticated key management schemes fail to provide effective conditional anonymity during decentralized authentication process. Meanwhile, scalable and reliable vehicular pseudonym management is absent, resulting in potential privacy leakage. Furthermore, conventional group key agreement schemes inherently fail to properly accommodate the highly dynamic topological characteristics of vehicular environments, which significantly limits their practical applicability. To address these challenges, the blockchain-assisted anonymous authentication and tree-based group key agreement design is proposed in this paper. Firstly, the pairing-free decentralized authentication mechanism is designed to enable mutual authentication between vehicles and roadside units (RSUs). Secondly, the threshold-varying pseudonym management system is designed, leveraging secret sharing and smart contracts to ensure conditional privacy preservation. This mechanism utilizes the multi-RSU consensus to recover the user's real identity, enabling traceability of malicious entities. Thirdly, the self-balancing tree-based group key agreement mechanism is proposed, optimizing key generation efficiency in dynamic vehicular environments. Crucial security requirements can be satisfied via the security analysis, whereas the performance evaluation substantiates the superiority of the proposed scheme over existing approaches.
The increasing reliance on smart grids to manage power distribution efficiently has introduced significant cybersecurity vulnerabilities due to their interconnected nature. Traditional security approaches often fall short in real-time protection, particularly against advanced threats such as data manipulation, unauthorized access, and Distributed Denial-of-Service (DDoS) attacks. This paper proposes a novel Smart Grid Secure Protocol (SGSP), integrating Attribute-Based Zero-Knowledge Proofs (AB-ZKP), Redundant Consensus Mechanisms combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) for scalable and fault-tolerant consensus, and Grid Safe Smart Contracts (GSSC) to enhance data confidentiality, automate security enforcement, and resist cyber threats. The AB-ZKP mechanism ensures selective attribute verification while preserving privacy and keeping sensitive data off-chain. The hybrid consensus mechanism merges energy-efficient PoS with fault-tolerant PBFT, securing the Blockchain layer against DDoS and Sybil attacks. Meanwhile, GSSCs automate transaction validation and policy enforcement, reducing human intervention and enabling real-time anomaly detection. Experimental results in a simulated environment demonstrate high resilience, improved data privacy (98.7% compliance), fast consensus (1.2 s), low energy consumption (0.09 kWh/transaction), and strong DDoS resistance (92.5/100). The proposed approach significantly outperforms traditional methods, paving the way for secure, scalable, and privacy-preserving smart grid ecosystems.
Douglas L. L. Moura, Andre L. L. Aquino, Antonio A. F. Loureiro
The integration of multiple distributed ledgers in Intelligent Transportation Systems (ITS) introduces challenges for scalable and interoperable authentication. Traditional schemes, which rely heavily on Public Key Infrastructure (PKI), face limitations related to certificate management and key escrow. To address these issues, we propose a federated authentication system based on certificateless public key cryptography (CL-PKC) to enable seamless cross-domain and cross-chain authentication without relying on traditional certificates. The proposed approach is designed to operate at the edge, where authentication is performed close to the user to reduce latency and support mobility. Leveraging the CL-PKC scheme, each user independently generates and manages their own cryptographic keys. Simulation results show reduced credential generation time, lower network usage, and improved latency under heavy and cross-domain conditions.
The rapid decentralization and digitalization of local electricity markets have introduced new cyber-physical vulnerabilities, including key leakage, data tampering, and identity spoofing. Existing blockchain-based solutions provide transparency and traceability but still depend on classical cryptographic primitives that are vulnerable to quantum attacks. To address these challenges, this paper proposes Q-EnergyDEX, a zero-trust distributed energy trading framework driven by quantum key distribution and blockchain. The framework integrates physical-layer quantum randomness with market-level operations, providing an end-to-end quantum-secured infrastructure. A cloud-based Quantum Key Management Service continuously generates verifiable entropy and regulates key generation through a rate-adaptive algorithm to sustain high-quality randomness. A symmetric authentication protocol (Q-SAH) establishes secure and low-latency sessions, while the quantum-aided consensus mechanism (PoR-Lite) achieves probabilistic ledger finality within a few seconds. Furthermore, a Stackelberg-constrained bilateral auction couples market clearing with entropy availability, ensuring both economic efficiency and cryptographic security. Simulation results show that Q-EnergyDEX maintains robust key stability and near-optimal social welfare, demonstrating its feasibility for large-scale decentralized energy markets.
Securing sensitive medical data in IoT-based healthcare systems is increasingly critical due to growing cybersecurity threats and the emergence of quantum computing. This paper addresses these challenges by proposing a decentralized architecture that integrates blockchain technology and postquantum cryptography (PQC) to ensure data integrity, privacy, and resilience. The solution employs the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm to maintain a tamper-resistant ledger of data locations, while encrypted medical records are distributed across Raspberry Pi nodes. Communications between sensor nodes and storage servers are protected using PQC primitives such as CRYSTALS-Kyber and Falcon. The methodology includes a lightweight protocol designed for resource-constrained environments and a storage scheme that optimizes scalability and energy efficiency. Experimental results validate the scalability and effectiveness of this architecture in real-world healthcare settings, demonstrating its potential as a secure and sustainable framework for electronic medical data management.
In response to the growing sophistication of cyber threats, traditional centralized authentication systems have become increasingly vulnerable, particularly to AI-driven attacks and large-scale system compromises. This paper proposes a novel blockchain-based authentication framework that integrates Byzantine Quorums (BQ) to enhance resilience, decentralization, and trust. The framework introduces a dynamic intersection-based verification mechanism, enabling accurate user authentication while continuously detecting and isolating malicious nodes. By leveraging the distributed and immutable nature of blockchain along with quorum-based consensus, the system significantly reduces the risks posed by Sybil, Eclipse, and double-spending attacks. Comparative analysis with existing frameworks demonstrates superior performance in terms of security, efficiency, and malicious node detection. The proposed solution holds strong potential for deployment in critical sectors such as finance, healthcare, and energy, where secure and scalable authentication is essential.
Exploration equipment for extreme environments like the Antarctic region constraints in power consumption, size, and weight. Furthermore, unmanned mobile exploration in environments with distributed IoET (Internet of Extreme Things) nodes requires long-range, delay-tolerant wireless communication. For these extreme environments, delay-tolerant communication systems can consider distributed ledgers as a way to record gains and losses to ensure coalition and reliability among nodes. However, Proof-of-Work (PoW), the most widely studied method for securing distributed ledger reliability, is simple to operate but highly energy-consumption. Proof-of-Stake (PoS) offers an energy-efficient alternative. This paper assumes a partially Δ-synchronized distributed system model for security analysis in PoS and analyzes the impact of network delays on the system. This analysis is an interpretation to identify methods for securing stability against balance attacks in public systems from the perspective of a partially Δ-synchronized model. The proposed technique is a game-theoretic approach that uses honest nodes to form a coalition to control delay. This study investigates the possibility of expanding the upper bound of the security region according to the attacker's occupation rate in a balanced attack by controlling the time delay required for nodes in a partially Δ-synchronized communication network to transmit messages to each other.
Russell W. F. Lai, Monisha Swarnakar, Ivy K. Y. Woo
The Learning with Errors (LWE) problem asks to distinguish noisy samples s^T A + e^T mod q from uniformly random values given the random matrix A. In this work, we show that a variant called Leaky LWE, where the distinguisher receives additionally noisy leakages (s^T, e^T) L + f^T of the LWE secret s and error e for low-norm matrix L chosen adaptively by the distinguisher after seeing A, is not easier than the standard LWE of the same dimensions up to polynomial losses in the noise level and the modulus. More generally, we show that the Leaky LWE problem is hard even if the public matrix A is structured and/or hinted and if the non-leaky parts of the secret and error do not follow Gaussian distributions, as long as the corresponding LWE problem without leakage is hard. Our reduction from LWE to Leaky LWE unifies and extends prior results on the Error-Leakage LWE problem [Döttling-Kolonelos-Lai-Lin-Malavolta-Rahimi, EUROCRYPT'23], where L only acts on the error e and the Hint-MLWE problem [Kim-Lee-Seo-Song, CRYPTO'23], where L is restricted to concatenations of random Gaussian scalar matrices not controlled by the distinguisher. Previously, the Hint-MLWE and Error-Leakage LWE assumptions were used as computational replacements of the statistical noise flooding technique in security proofs which led to improved parameters in lattice-based cryptographic constructions such as zero-knowledge proofs, threshold signatures and registration-based encryption. We provide lemmas which abstract out such computational arguments based on Leaky LWE.
Wireless Sensor Networks (WSNs) are widely used in various applications that require secure and efficient data aggregation. A novel secure data aggregation scheme is proposed in this study. This is a novel combination of Blockchain technology and Zero-Knowledge Proofs (ZKPs). This scheme is broken down into three parts. The first is Blockchain based Data Aggregation for storage of tamper proof and immutable data. Second, anonymity with ZKP to provide privacy preservation and secure identity verification. Finally, we combined Blockchain and ZKP operation to achieve energy efficiency and robust security. The proposed scheme guarantees data confidentiality, integrity, anonymity, and resilience to malicious attacks, and at the same time handles the energy optimization problem in WSNs. We implemented the proposed scheme in MATLAB to evaluate its performance with metrics like stability, jitter, latency, and throughput. Simulation results show that the proposed scheme achieved robust security in terms of data integrity and confidentiality by slightly compromising on the availability aspect.