ZAPS: A Zero-Knowledge Proof Protocol for Secure UAV Authentication with Flight Path Privacy
- URL: http://arxiv.org/abs/2508.17043v1
- Date: Sat, 23 Aug 2025 14:45:25 GMT
- Title: ZAPS: A Zero-Knowledge Proof Protocol for Secure UAV Authentication with Flight Path Privacy
- Authors: Shayesta Naziri, Xu Wang, Guangsheng Yu, Christy Jie Liang, Wei Ni,
- Abstract summary: Existing encryption techniques provide security but fail to ensure complete privacy.<n>We propose a zk-SNARK-based privacy-preserving flight path authentication and verification framework.<n>Our solution balances privacy, security, and computational efficiency, making it suitable for resource-constrained UAVs in both civilian and military applications.
- Score: 17.583821328586904
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The increasing deployment of Unmanned Aerial Vehicles (UAVs) for military, commercial, and logistics applications has raised significant concerns regarding flight path privacy. Conventional UAV communication systems often expose flight path data to third parties, making them vulnerable to tracking, surveillance, and location inference attacks. Existing encryption techniques provide security but fail to ensure complete privacy, as adversaries can still infer movement patterns through metadata analysis. To address these challenges, we propose a zk-SNARK(Zero-Knowledge Succinct Non-Interactive Argument of Knowledge)-based privacy-preserving flight path authentication and verification framework. Our approach ensures that a UAV can prove its authorisation, validate its flight path with a control centre, and comply with regulatory constraints without revealing any sensitive trajectory information. By leveraging zk-SNARKs, the UAV can generate cryptographic proofs that verify compliance with predefined flight policies while keeping the exact path and location undisclosed. This method mitigates risks associated with real-time tracking, identity exposure, and unauthorised interception, thereby enhancing UAV operational security in adversarial environments. Our proposed solution balances privacy, security, and computational efficiency, making it suitable for resource-constrained UAVs in both civilian and military applications.
Related papers
- Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
arXiv Detail & Related papers (2026-01-16T18:44:09Z) - 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) - Unfolding Challenges in Securing and Regulating Unmanned Air Vehicles [2.919142943489536]
We conduct a comprehensive state-of-the-art study and examine the prevailing security challenges.<n>Unlike the prior art, we focus on uncovering the research gaps that must be addressed to enforce security policy regulations.
arXiv Detail & Related papers (2025-12-03T13:41:30Z) - Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
arXiv Detail & Related papers (2025-10-28T10:05:53Z) - 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) - Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial [107.26005706569498]
Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics.<n>Generative AI (GAI) is a transformative approach that can safeguard SAGIN security by synthesizing data, understanding semantics, and making autonomous decisions.
arXiv Detail & Related papers (2025-08-04T01:42:57Z) - CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
arXiv Detail & Related papers (2025-05-14T13:37:07Z) - Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey [76.36166980302478]
The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm.<n>Physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments.<n>This survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet.
arXiv Detail & Related papers (2025-04-12T09:36:53Z) - An Approach to Technical AGI Safety and Security [72.83728459135101]
We develop an approach to address the risk of harms consequential enough to significantly harm humanity.<n>We focus on technical approaches to misuse and misalignment.<n>We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
arXiv Detail & Related papers (2025-04-02T15:59:31Z) - Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning [72.72954660774002]
Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas.<n>Extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks.<n>This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle.
arXiv Detail & Related papers (2025-01-26T10:13:51Z) - Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation [0.0]
This paper proposes an innovative approach that leverages Knowledge Distillation to enhance the practicality of secure UAV navigation.
By integrating RL and FHE, our framework addresses vulnerabilities to adversarial attacks while enabling real-time processing of encrypted UAV camera feeds.
To mitigate FHE's latency, Knowledge Distillation is employed to compress the network, resulting in an impressive 18x speedup without compromising performance.
arXiv Detail & Related papers (2024-11-01T07:04:24Z) - Securing UAV Communication: Authentication and Integrity [0.0]
We propose an authentication method to secure UAV data exchange over an insecure communication channel.
Our solution combines Diffie-Hellman key exchange and Hash-based Message Authentication Code (HMAC) within ROS communication channels.
Both drones successfully detected tampered keys, affirming our method's efficacy in protecting UAV communication.
arXiv Detail & Related papers (2024-10-06T22:36:06Z) - Enhancing Privacy and Security of Autonomous UAV Navigation [0.8512184778338805]
In critical scenarios such as border protection or disaster response, ensuring the secure navigation of autonomous UAVs is paramount.
We propose an innovative approach that combines Reinforcement Learning (RL) and Fully Homomorphic Encryption (FHE) for secure autonomous UAV navigation.
Our proposed approach ensures security and privacy in autonomous UAV navigation with negligible loss in performance.
arXiv Detail & Related papers (2024-04-26T07:54:04Z) - Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading [3.427366431933441]
Our framework incorporates exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and jamming threats.
We introduce a transformer-enhanced Deep Reinforcement Learning (DRL) approach to optimize task offloading processes.
arXiv Detail & Related papers (2024-04-06T17:41:00Z) - 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) - VBSF-TLD: Validation-Based Approach for Soft Computing-Inspired Transfer
Learning in Drone Detection [0.0]
This paper presents a transfer-based drone detection scheme, which forms an integral part of a computer vision-based module.
By harnessing the knowledge of pre-trained models from a related domain, transfer learning enables improved results even with limited training data.
Notably, the scheme's effectiveness is highlighted by its IOU-based validation results.
arXiv Detail & Related papers (2023-06-11T22:30:23Z)
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.