Privacy protection method for ADS-B air traffic control data based on convolutional neural network and symmetric encryption

Changsheng Ma ¹, Ruchun Jia ², Jing Lou ¹

⁰School of Information Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, Jiangsu, China.

Frontiers in Big Data +

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January 5, 2026

View abstract on PubMed

Summary

This study introduces a novel privacy protection method for Automatic Dependent Surveillance-Broadcast (ADS-B) data, combining deep learning and encryption. The approach effectively safeguards sensitive flight information with efficient encryption times.

Area Of Science

Air Traffic Management
Cybersecurity
Data Privacy

Background

Automatic Dependent Surveillance-Broadcast (ADS-B) is crucial for modern air traffic management, providing real-time flight data.
The open nature of ADS-B broadcasts poses significant privacy risks due to potential data interception and misuse.
Effective mining and safeguarding of privacy information in ADS-B data present critical research challenges.

Purpose Of The Study

To propose a novel privacy protection method for ADS-B air traffic control data.
To address the challenges of data interception and misuse in ADS-B systems.
To enhance the security and privacy of sensitive flight information.

Main Methods

Integration of deep learning techniques with symmetric encryption.
Analysis of ADS-B air traffic monitoring architecture to identify and normalize privacy-related data.
Development of a Convolutional Neural Network (CNN)-based classification model for sensitive information identification.

Main Results

The proposed method effectively scrambles original privacy information, preventing data theft or damage.
Demonstrated efficiency in encryption times across various data volumes (10GB-40GB), with times ranging from 20.36ms to 50.36ms.
Achieved robust privacy protection with shorter encryption times compared to existing methods.

Conclusions

The novel method offers an effective solution for privacy protection in ADS-B systems.
The integration of deep learning and symmetric encryption provides efficient and robust data security.
Future research should focus on advanced encryption and deep learning for enhanced ADS-B privacy protection.

Keywords:

ADS-B air traffic control data convolutional neural network deep learning privacy protection symmetric encryption

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