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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks.

Chaoyang Zhu^1,2, Xiao Zhu³, Tuanfa Qin^2,4

¹School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

We introduce UBFL, a privacy-preserving federated learning system for UAVs in smart cities. It uses blockchain and adaptive encryption to secure data, outperforming traditional methods in accuracy and resilience against attacks.

Keywords:

blockchain data privacy federated learning poisoning attack unmanned aerial vehicles

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Unmanned Aerial Vehicles (UAVs) in smart cities raise privacy and security concerns due to sensitive data transmission.
Centralized data processing in traditional UAV-Mobile Edge Computing (MEC) systems risks data breaches and manipulation.
These risks can impede the adoption of beneficial UAV technologies.

Purpose of the Study:

To propose UBFL, a novel privacy-preserving federated learning mechanism for UAVs.
To enhance data security and integrity in UAV-MEC systems using blockchain technology.
To address limitations of traditional differential privacy (DP) methods.

Main Methods:

UBFL integrates blockchain for secure data sharing and an adaptive nonlinear encryption function for privacy.
It enables efficient filtering of compromised UAVs by the base station.
The Random Cut Forest (RCF) algorithm is used for anomaly detection and mitigation of poisoning attacks.

Main Results:

UBFL demonstrates superior accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) compared to DP methods.
The system shows exceptional resilience against data pollution attacks at rates of 10%, 20%, and 30%.
Experiments on CIFAR10 and Mnist datasets validate UBFL's effectiveness.

Conclusions:

UBFL offers a robust solution for securing UAV gradients in MEC environments.
The proposed mechanism effectively preserves privacy while maintaining high data accuracy.
UBFL enhances the security and reliability of UAV applications in smart cities.