Deep Learning Model for Privacy Preservation of Vehicle Trajectories Over Internet of Vehicles
Deep Learning Model for Privacy Preservation of Vehicle Trajectories Over Internet of Vehicles |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-12 |
||
| Year of Publication : 2024 | ||
| Author : Nikhil Chaurasia, Pritaj Yadav, Sanjeev Kumar Gupta |
||
| DOI : 10.14445/22315381/IJETT-V72I12P113 | ||
How to Cite?
Nikhil Chaurasia, Pritaj Yadav, Sanjeev Kumar Gupta, "Deep Learning Model for Privacy Preservation of Vehicle Trajectories Over Internet of Vehicles," International Journal of Engineering Trends and Technology, vol. 72, no. 12, pp. 140-150, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I12P113
Abstract
This research presents a deep learning-based differential privacy Laplace mechanism (DLPM) for the networking trajectories of the Internet of Vehicles. The DLPM is constructed using deep learning and clustering techniques to address consumers’ privacy leakage concerns effectively. Segment the trajectory space into separate regions based on timestamps to incorporate the temporal element in trajectories. This will determine the trajectory’s distribution points inside each zone. Enhance membership stability in multi-peak clustering for each region and pre-allocate the privacy budget matrix based on the trajectory density of each area. Employ a temporal graph convolutional network model to train and predict the designated privacy budget matrix while extracting the spatiotemporal characteristics of trajectory data. Disseminate the trajectory data just after applying Laplace noise to the predictive outcomes. Both theoretical and empirical research indicate that DLPM exhibits reduced overhead and more precise privacy budget predictions than alternative systems. The Differential Privacy Laplace Mechanism (DLPM) incorporates Laplace noise into trajectory data, enhancing its use.
Keywords
Internet of Things, Privacy protection, Connected vehicle, Trajectory, Clustering, Deep learning.
References
[1] Olusogo Popoola et al., “A Critical Literature Review of Security and Privacy in Smart Home Healthcare Schemes Adopting IoT & Blockchain: Problems, Challenges and Solutions,” Blockchain: Research and Applications, vol. 5, no. 2, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Yichen Wan et al., “Privacy-Preserving Blockchain-Enabled Federated Learning for B5G-Driven Edge Computing,” Computer Networks, vol. 204, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ikram Ud Din et al., “Machine Learning in the Internet of Things: Designed Techniques for Smart Cities,” Future Generation Computer Systems, vol. 100, pp. 826-843, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Praveen Kumar Reddy Maddikunta et al., “Incentive Techniques for the Internet of Things: A survey,” Journal of Network and Computer Applications, vol. 206, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Aqeel Thamer Jawad, Rihab Maaloul, and Lamia Chaari, “A Comprehensive Survey on 6G and Beyond: Enabling Technologies, Opportunities of Machine Learning and Challenges,” Computer Networks, vol. 237, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mubarak Umar et al., “Physical Layer Authentication in the Internet of Vehicles through Multiple Vehicle-Based Physical Attributes Prediction,” Ad Hoc Networks, vol. 152, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Jie Liu et al., “Internet of Things Challenges and Future Scope for Enhanced Living Environments,” Advances in Computers, vol. 133, pp. 201-246, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Atefeh Hemmati, Mani Zarei, and Alireza Souri, “UAV-Based Internet of Vehicles: A Systematic Literature Review,” Intelligent Systems with Applications, vol. 18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Hamed Alqahtani, and Gulshan Kumar, “Machine Learning for Enhancing Transportation Security: A Comprehensive Analysis of Electric and Flying Vehicle Systems,” Engineering Applications of Artificial Intelligence, vol. 129, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jean-Paul A. Yaacoub, Hassan N. Noura, and Benoit Piranda, “The Internet of Modular Robotic Things: Issues, Limitations, Challenges, & Solutions, Internet of Things, vol. 23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Homayun Kabir, Mau-Luen Tham, and Yoong Choon Chang, “Internet of Robotic Things for Mobile Robots: Concepts, Technologies, Challenges, Applications, and Future Directions,” Digital Communications and Networks, vol. 9, no. 6, pp. 1265-1290, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Nisha, and Urvashi, “A Systematic Literature Review of Internet of Video Things: Trends, Techniques, Datasets, and Framework,” Internet of Things, vol. 24, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hongwei Zhang, Wei Fan, and Jinsong Wang, “Bidirectional Utilization of Blockchain and Privacy Computing: Issues, Progress, and Challenges,” Journal of Network and Computer Applications, vol. 222, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Chenxi Huang et al., “Internet of Medical Things: A Systematic Review,” Neurocomputing, vol. 557, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Lu Chen et al., “An Approach of Flow Compensation Incentive Based on Q-Learning Strategy for IoT User Privacy Protection,” AEU - International Journal of Electronics and Communications, vol. 148, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Guowen Wu et al., “Privacy-Preserving Offloading Scheme in Multi-Access Mobile Edge Computing Based on MADRL,” Journal of Parallel and Distributed Computing, vol. 183, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Mirsaeid Hosseini Shirvani, and Mohammad Masdari, “A Survey Study on Trust-Based Security in Internet of Things: Challenges and Issues,” Internet of Things, vol. 21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Abderahman Rejeb et al., “Unleashing the Power of Internet of Things and Blockchain: A Comprehensive Analysis and Future Directions,” Internet of Things and Cyber-Physical Systems, vol. 4, pp. 1-18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Heng Li et al., “IoB: Internet-of-Batteries for Electric Vehicles-Architectures, Opportunities, and Challenges, Green Energy and Intelligent Transportation, vol. 2, no. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Sabrina Sicari, Alessandra Rizzardi, and Alberto Coen-Porisini, “5G in the Internet of Things Era: An Overview on Security and Privacy Challenges,” Computer Networks, vol. 179, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Godwin Badu-Marfo et al., “An Ensemble Federated Learning Framework for Privacy-by-Design Mobility Behaviour Inference in Smart Cities,” Sustainable Cities and Society, vol. 97, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Xiaolan Tang et al., “Assisted Driving System Based on Federated Reinforcement Learning,” Displays, vol. 80, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Ali Akbar Siddique et al., “Sustainable collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT),” Internet of Things, vol. 25, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Rasha Al-Huthaifi et al., “Federated Learning in Smart Cities: Privacy and Security Survey,” Information Sciences, vol. 632, pp. 833-857, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Izhar Ahmed Khan et al., “Fed-Inforce-Fusion: A Federated Reinforcement-Based Fusion Model for Security and Privacy Protection of IoMT Networks Against Cyber-Attacks,” Information Fusion, vol. 101, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Yanling Wang et al., “Differential Privacy in Deep Learning: Privacy and Beyond,” Future Generation Computer Systems, vol. 148, pp. 408-424, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Jong Wook Kim, and Beakcheol Jang, “Deep Learning-Based Privacy-Preserving Framework for Synthetic Trajectory Generation,” Journal of Network and Computer Applications, vol. 206, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Yi-rui Huang et al., “GeoPM-DMEIRL: A Deep Inverse Reinforcement Learning Security Trajectory Generation Framework with Serverless Computing,” Future Generation Computer Systems, vol. 154, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Sujie Shao et al., “Multi Task Dynamic Edge-End Computing Collaboration for Urban Internet of Vehicles,” Computer Networks, vol. 227, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Raiful Hasan, and Ragib Hasan, “Pedestrian Safety Using the Internet of Things and Sensors: Issues, Challenges, and Open Problems, Future Generation Computer Systems, vol. 134, pp. 187-203, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Yassine Himeur et al., “Edge AI for Internet of Energy: Challenges and perspectives,” Internet of Things, vol. 25, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Prashant Singh et al., “Internet of Things for Sustainable Railway Transportation: Past, Present, and Future,” Cleaner Logistics and Supply Chain, vol. 4, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Nayan Kumar Subhashis Behera et al., “Futuristic Person Re-Identification over Internet of Biometrics Things (IoBT): Technical Potential versus Practical Reality,” Pattern Recognition Letters, vol. 151, pp. 163-171, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Shahnawaz Ahmad et al., “Deep Learning Models for Cloud, Edge, Fog, and IoT Computing Paradigms: Survey, Recent Advances, and Future Directions,” Computer Science Review, vol. 49, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Chen Fang et al., “A Privacy-Preserving and Verifiable Federated Learning Method Based on Blockchain,” Computer Communications, vol. 186, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Francine Berman et al., “The Impact Universe-A Framework for Prioritizing the Public Interest in the Internet of Things,” Patterns, vol. 3, no. 1, 2022.
[Google Scholar] [Publisher Link]
[37] Sofiane Zaidi, Mohammed Atiquzzaman, and Carlos T. Calafate, “Internet of Flying Things (IoFT): A Survey,” Computer Communications, vol. 165, pp. 53-74, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Yalin Liu et al., “Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges,” Computer Communications, vol. 155, pp. 66-83, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Tao Chang et al., “PAGroup: Privacy-Aware Grouping Framework for High-Performance Federated Learning,” Journal of Parallel and Distributed Computing, vol. 175, pp. 37-50, 2023.
[CrossRef] [Google Scholar] [Publisher Link]