Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems
An intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information sha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/115 |
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| author | Meshari D. Alanazi Mohammed Albekairi Ghulam Abbas Turki M. Alanazi Khaled Kaaniche Gehan Elsayed Paolo Mercorelli |
| author_facet | Meshari D. Alanazi Mohammed Albekairi Ghulam Abbas Turki M. Alanazi Khaled Kaaniche Gehan Elsayed Paolo Mercorelli |
| author_sort | Meshari D. Alanazi |
| collection | DOAJ |
| description | An intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information shared through the communication medium in SCs is exposed to adversary risk, resulting in privacy issues. Privacy issues impact the contingent mobility and localization of the ITS path. This paper introduces a novel resilient privacy preserving (RPP) method through presumed secrecy (PS) to provide a robust privacy measure. The privacy of the progressive communication sessions is preserved based on the previous security depletion levels. The interruptions in traffic data-related communication sessions are recurrently identified, and re-handoffs are recommended with dodged transfer learning. The empirical results indicate a 25% reduction in computational overhead and a 30% enhancement in privacy protection over conventional methods, demonstrating the model’s efficacy in secure ITS communication. Compared with existing methods, the proposed approach decreases security depletion rates by 15% across varying traffic densities, underscoring ITS resilience in high-interaction scenarios. |
| format | Article |
| id | doaj-art-70d7d8b0135e40a2b151ad3581c32338 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-70d7d8b0135e40a2b151ad3581c323382025-01-10T13:20:55ZengMDPI AGSensors1424-82202024-12-0125111510.3390/s25010115Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation SystemsMeshari D. Alanazi0Mohammed Albekairi1Ghulam Abbas2Turki M. Alanazi3Khaled Kaaniche4Gehan Elsayed5Paolo Mercorelli6Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi ArabiaSchool of Electrical Engineering, Southeast University, Nanjing 210096, ChinaDepartment of Electrical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi ArabiaDepartment of Interior Design, College of Engineering, Jouf University, Sakakah 72388, Saudi ArabiaInstitute for Production Technology and Systems (IPTS), Leuphana Universität Lüneburg, 21335 Lüneburg, GermanyAn intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information shared through the communication medium in SCs is exposed to adversary risk, resulting in privacy issues. Privacy issues impact the contingent mobility and localization of the ITS path. This paper introduces a novel resilient privacy preserving (RPP) method through presumed secrecy (PS) to provide a robust privacy measure. The privacy of the progressive communication sessions is preserved based on the previous security depletion levels. The interruptions in traffic data-related communication sessions are recurrently identified, and re-handoffs are recommended with dodged transfer learning. The empirical results indicate a 25% reduction in computational overhead and a 30% enhancement in privacy protection over conventional methods, demonstrating the model’s efficacy in secure ITS communication. Compared with existing methods, the proposed approach decreases security depletion rates by 15% across varying traffic densities, underscoring ITS resilience in high-interaction scenarios.https://www.mdpi.com/1424-8220/25/1/115forward secrecyintelligent transportationsmart citiestransfer learningmachine learning |
| spellingShingle | Meshari D. Alanazi Mohammed Albekairi Ghulam Abbas Turki M. Alanazi Khaled Kaaniche Gehan Elsayed Paolo Mercorelli Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems Sensors forward secrecy intelligent transportation smart cities transfer learning machine learning |
| title | Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems |
| title_full | Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems |
| title_fullStr | Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems |
| title_full_unstemmed | Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems |
| title_short | Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems |
| title_sort | resilient privacy preservation through a presumed secrecy mechanism for mobility and localization in intelligent transportation systems |
| topic | forward secrecy intelligent transportation smart cities transfer learning machine learning |
| url | https://www.mdpi.com/1424-8220/25/1/115 |
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