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

Sep 28, 2026

ADS-B (Automatic Dependent Surveillance-Broadcast) is central to modern air traffic management by broadcasting real-time aircraft information such as position, speed, and altitude. Its open broadcast nature creates major privacy risks, since sensitive data can be intercepted and misused. The study introduces a privacy protection approach that mines and normalizes privacy-related data and trains a CNN-based classification model, then scrambles sensitive information using symmetric encryption. Experiments indicate efficient encryption times across multiple data volumes while preserving privacy and avoiding theft or malicious damage.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Methods

Results

Discussion

TYPE Original Research PUBLISHED 18 December 2025 DOI 10. 3389/fdata.2025.1683027 OPEN ACCESS EDITED BY Pradeep Kumar, Manipal University Jaipur, India REVIEWED BY Geeta Sandeep Nadella, University of the Cumberlands, United States Disha Handa, Chandigarh University, India Jalpesh Vasa, Charotar University of Science and Technology Chandubhai S Patel Institute of Technology, India *CORRESPONDENCE Ruchun Jia [jiaruchun@stu.scu.edu.cn](jiaruchun@stu.scu.edu.cn) RECEIVED 13 August 2025 REVISED 24 November 2025 ACCEPTED 30 November 2025 PUBLISHED 18 December 2025 CITATION Ma C, Jia R, Lou J and Wang M (2025) Privacy protection method for ADS-B air traffic control data based on convolutional neural network and symmetric encryption. Front. Big Data 8:1683027 . doi: 10.3389/fdata.2025.1683027 COPYRIGHT © 2025 Ma, Jia, Lou and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Privacy protection method for ADS-B air traffic control data based on convolutional neural network and symmetric encryption Changsheng Ma1 , Ruchun Jia2*, Jing Lou1 and Mingqian Wang1 1 School of Information Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, Jiangsu, China, 2 College of Computer Science, Sichuan University, Chengdu, Sichuan, China Introduction: ADS-B (Automatic Dependent Surveillance-Broadcast) is a key surveillance technology in modern air traffic management, which broadcasts real-time aircraft information such as position, speed, and altitude for enhanced ﬂight tracking and safety. However, the open broadcast nature of ADS-B communication raises signiﬁcant privacy concerns, as sensitive data can be easily intercepted and misused. Research on privacy protection for ADS-B air traffic control data faces signiﬁcant challenges, making the effective mining and safeguarding of privacy information a critical research focus. Methods: This study proposes a novel privacy protection method that integrates deep learning with symmetric encryption. Speciﬁcally, by analyzing the ADS-B air traffic monitoring architecture, we mine and normalize privacy-related data to develop a Convolutional Neural Network (CNN)-based classiﬁcation model for accurate identiﬁcation of sensitive information. Results: Experimental results demonstrate that the proposed method effectively scrambles the original privacy information, with no instances of data theft or malicious damage. For data volumes of 10GB, 20GB, 30GB, and 40GB, the encryption times are 20.36ms, 30.56ms, 40.35ms, and 50.36ms, respectively, showcasing its efficiency. Discussion: Compared to existing methods, our approach achieves shorter encryption times while maintaining robust privacy protection. Future work could explore integrating advanced encryption technologies with state-of-the-art deep learning algorithms to further enhance the security of privacy protection in ADS-B systems. KEYWORDS privacy protection, ADS-B air traffic control data, deep learning, symmetric encryption, convolutional neural network 1 Introduction The Automatic Dependent Surveillance-Broadcast (ADS-B) system, as a core component of the next-generation air traﬃc management, relies on data link broadcasting technology to transmit key operational information (Casqueiro et al., 2023)—such as aircraft identiﬁcation codes, latitude/longitude, altitude, velocity, and heading—in realtime. This capability signiﬁcantly enhances airspace operational eﬃciency and ﬂight safety. However, while advancing the modernization of air traﬃc management, the technolog

FAQs

What privacy problem does the study address in ADS-B systems?

ADS-B broadcasts real-time aircraft data openly, making sensitive information vulnerable to interception and misuse during transmission and sharing.

How does the proposed method protect privacy?

It integrates CNN-based sensitive information classification with symmetric encryption to mine, normalize, and scramble privacy-related data.

What do the experimental results show about efficiency and effectiveness?

The method effectively scrambles original privacy information, and reported encryption times increase with data volume (10GB to 40GB), indicating workable efficiency for the tested settings.

Sep 28, 2026

Upload by Genevieve