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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. 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Kumar,  \nManipal University Jaipur, India  \nREVIEWED BY  \nGeeta Sandeep Nadella,  \nUniversity of the Cumberlands, United States Disha Handa,  \nChandigarh University, India Jalpesh Vasa,  \nCharotar University of Science and Technology Chandubhai S Patel Institute of Technology, India  \n*CORRESPONDENCE  \nRuchun Jia  \n [jiaruchun@stu.scu.edu.cn](jiaruchun@stu.scu.edu.cn)  \nRECEIVED 13 August 2025  \nREVISED 24 November 2025  \nACCEPTED 30 November 2025  \nPUBLISHED 18 December 2025  \nCITATION  \nMa 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.  \nFront. Big Data 8:1683027 .  \ndoi: 10.3389/fdata.2025.1683027  \nCOPYRIGHT  \n© 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.  \nPrivacy protection method for ADS-B air traffic control data based on convolutional neural network and symmetric encryption  \nChangsheng Ma1 , Ruchun Jia2*, Jing Lou1 and Mingqian Wang1  \n1 School of Information Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, Jiangsu, China, 2 College of Computer Science, Sichuan University, Chengdu, Sichuan, China  \nIntroduction: 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.  \nMethods: 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.  \nResults: 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.  \nDiscussion: 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.  \nKEYWORDS  \nprivacy protection, ADS-B air traffic control data, deep learning, symmetric encryption, convolutional neural network  \n1 Introduction  \nThe 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","cbCaigTGu6CR8TkT","https://ap.wps.com/l/cbCaigTGu6CR8TkT","pdf",3083856,17,"English","# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"What privacy problem does the study address in ADS-B systems?\",\"answer\":\"ADS-B broadcasts real-time aircraft data openly, making sensitive information vulnerable to interception and misuse during transmission and sharing.\"},{\"question\":\"How does the proposed method protect privacy?\",\"answer\":\"It integrates CNN-based sensitive information classification with symmetric encryption to mine, normalize, and scramble privacy-related data.\"},{\"question\":\"What do the experimental results show about efficiency and effectiveness?\",\"answer\":\"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.\"}]","Privacy protection method for ADS-B air traffic control data based on convolutional neural network and symmetric encryption | PDF",43]