Blockchain-Driven AI-Enhanced Post-Quantum Multivariate Identity-based Signature and Privacy-Preserving Data Aggregation Scheme for Fog-enabled Flying Ad-Hoc Networks

• URL: http://arxiv.org/abs/2604.18819v1
• Date: Mon, 20 Apr 2026 20:40:55 GMT
• Title: Blockchain-Driven AI-Enhanced Post-Quantum Multivariate Identity-based Signature and Privacy-Preserving Data Aggregation Scheme for Fog-enabled Flying Ad-Hoc Networks
• Abstract summary: Integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications.<n>This work introduces a blockchain-based, AI-enhanced key management framework designed for fog-enabled FANETs.
• Score: 0.11199585259018456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications. However, the distributed nature, mobility, and resource constraints of FANETs expose them to significant security and privacy challenges, particularly against quantum threats. To address these issues, this work introduces a blockchain-based, AI-enhanced key management framework designed for fog-enabled FANETs. The proposed scheme employs a Post-Quantum Multivariate Identity-Based Signature Scheme (PQ-MISS) and Zero-Knowledge Proofs (ZKPs) to achieve secure key establishment, privacy-preserving data aggregation, and integrity verification. A polynomial composition-based encryption mechanism and an aggregate signature model support secure and efficient multi-device communication across fog and UAV layers. Fog servers construct partial blockchain blocks from validated UAV data. These blocks are completed and mined by Cloud Servers (CSs). AI algorithms then analyze the verified data to generate accurate predictions and insights. NS-3 simulations validate the efficiency of PQ-MISS in reducing communication overhead while improving the speed and reliability of data aggregation and verification. Comparative analysis demonstrates the proposed scheme's advantages over existing methods in computational cost, post-quantum security, and scalability, making it a robust solution for secure, intelligent, and future-ready FANET systems.

Related papers

• Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning [2.0965265618008315]
  passive, location-aware, static eavesdroppers without access to legitimate channel state information (CSI)<n>We propose secure adaptive authentication for multi-zone systems (SAFA-MZ) that maximizes secrecy spectral efficiency (SSE)<n>Results under both colluding and non-colluding eavesdroppers show that the proposed method improves average SSE by up to 135% over single-connect transmission and 21% over the scheme without AN.
  arXiv  Detail & Related papers  (2026-09-08T21:46:12Z)
• Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework [45.88028371034407]
  This paper presents a structured analysis of privacy-preserving techniques for distributed learning in IoT environments.<n>A unified threat model is introduced that captures model inversion, membership inference, gradient leakage, and communication-based attacks.<n>The results highlight a fundamental trade-off between privacy strength and system efficiency.
  arXiv  Detail & Related papers  (2026-05-10T00:22:11Z)
• A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
  Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
  arXiv  Detail & Related papers  (2026-02-23T16:12:02Z)
• Multi-Agent-Driven Cognitive Secure Communications in Satellite-Terrestrial Networks [58.70163955407538]
  Malicious eavesdroppers pose a serious threat to private information via satellite-terrestrial networks (STNs)<n>We propose a cognitive secure communication framework driven by multiple agents that coordinates spectrum scheduling and protection through real-time sensing.<n>We exploit generative adversarial networks to produce adversarial matrices, and employ learning-aided power control to set real and adversarial signal powers for protection layer.
  arXiv  Detail & Related papers  (2026-01-06T10:30:41Z)
• When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks [68.45202147860537]
  Low-altitude wireless networks (LAWNs) provide enhanced coverage, reliability, and throughput for diverse applications.<n>These networks face significant security vulnerabilities from both known and potential unknown eavesdroppers.<n>We propose a novel secure communication framework for LAWNs where the selected UAVs within a swarm function as a virtual antenna array.
  arXiv  Detail & Related papers  (2025-10-25T02:02:14Z)
• Secure and Scalable Blockchain Voting: A Comparative Framework and the Role of Large Language Models [0.0]
  This paper presents a comparative framework for analyzing blockchain-based E-Voting architectures, consensus mechanisms, and cryptographic protocols.<n>We propose optimization strategies that include hybrid consensus, lightweight cryptography, and decentralized identity management.<n>Our findings offer a foundation for designing secure, scalable, and intelligent blockchain-based E-Voting systems suitable for national-scale deployment.
  arXiv  Detail & Related papers  (2025-08-07T21:34:21Z)
• Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment [0.559239450391449]
  We propose an autonomous computing model for privacy-critical and time-sensitive health applications.<n>The system supports continuous monitoring, real-time alert notifications, disease detection, and robust data processing and aggregation.<n>A secure access scheme is defined to manage both off-chain and on-chain data sharing and storage.
  arXiv  Detail & Related papers  (2025-05-31T06:58:52Z)
• Quantum-Resilient Blockchain for Secure Transactions in UAV-Assisted Smart Agriculture Networks [0.0]
  The integration of unmanned aerial vehicles (UAVs) into smart agriculture has enabled real-time monitoring, data collection, and automated farming operations.<n>This paper presents a quantum-resilient blockchain framework designed to secure data and resource transactions in UAV-assisted smart agriculture networks.
  arXiv  Detail & Related papers  (2025-05-21T17:12:39Z)
• AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles [1.1797787239802762]
  We develop a new framework that combines the power of Artificial Intelligence (AI) for real-time anomaly detection with blockchain technology to detect and prevent any malicious activity.<n>This framework employs a decentralized platform for securely storing sensor data and anomaly alerts in a blockchain ledger for data incorruptibility and authenticity.<n>This makes the AV system more resilient to attacks from both cyberspace and hardware component failure.
  arXiv  Detail & Related papers  (2025-05-10T12:53:28Z)
• Trusted Compute Units: A Framework for Chained Verifiable Computations [41.94295877935867]
  This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable computations across heterogeneous technologies.<n>By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability.
  arXiv  Detail & Related papers  (2025-04-22T09:01:55Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• Towards Secure and Private AI: A Framework for Decentralized Inference [14.526663289437584]
  Large multimodal foundational models present challenges in scalability, reliability, and potential misuse.<n>Decentralized systems offer a solution by distributing workload and mitigating central points of failure.<n>We address these challenges with a comprehensive framework designed for responsible AI development.
  arXiv  Detail & Related papers  (2024-07-28T05:09:17Z)
• A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• ScionFL: Efficient and Robust Secure Quantized Aggregation [36.668162197302365]
  We introduce ScionFL, the first secure aggregation framework for federated learning. It operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients. We show that with no overhead for clients and moderate overhead for the server, we obtain comparable accuracy for standard FL benchmarks.
  arXiv  Detail & Related papers  (2022-10-13T21:46:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.