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Ensuring Situation-Aware Privacy for Connected Vehicles

Published: 05 January 2023 Publication History

Abstract

As technology advances in new sensors and software, modern vehicles become increasingly intelligent. To date, connected vehicles can collect, process, and share data with other entities in connected vehicle environments. However, in terms of data collection and exchange, privacy becomes a central issue. It is challenging to preserve privacy in connected vehicle environments when the privacy demands of drivers could change from situation to situation even for the same service. In this paper, we analyze the requirements for a privacy-preserving system in connected vehicle environments with a focus on situation-awareness and safety aspects. Based on the analysis, we propose a novel situation-aware privacy-preserving framework for connected vehicles. Our framework supports individual privacy protections for specific end-point services and situation-aware privacy protections for different circumstances.

References

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Mumin Cebe, Enes Erdin, Kemal Akkaya, Hidayet Aksu, and Selcuk Uluagac. 2018. Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles. IEEE Communications Magazine 56, 10 (2018), 50–57.
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Riccardo Coppola and Maurizio Morisio. 2016. Connected Car: Technologies, Issues, Future Trends. ACM Comput. Surv. 49, 3, Article 46 (oct 2016), 36 pages.
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Sastry Duri, Marco Gruteser, Xuan Liu, Paul Moskowitz, Ronald Perez, Moninder Singh, and Jung-Mu Tang. 2002. Framework for Security and Privacy in Automotive Telematics. In Proceedings of the 2nd International Workshop on Mobile Commerce (Atlanta, Georgia, USA) (WMC ’02). Association for Computing Machinery, New York, NY, USA, 25–32.
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Soheila Ghane, Alireza Jolfaei, Lars Kulik, Kotagiri Ramamohanarao, and Deepak Puthal. 2021. Preserving Privacy in the Internet of Connected Vehicles. IEEE Transactions on Intelligent Transportation Systems 22, 8(2021), 5018–5027.
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Christian Plappert, Daniel Zelle, Christoph Krauß, Benjamin Lange, S Mauthöfer, Jonas Walter, Bettina Abendroth, Rasmus Robrahn, Thilo von Pape, and Hendrik Decke. 2017. A privacy-aware data access system for automotive applications. In 15th ESCAR Embedded Security in Cars Conference.
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Joshua E. Siegel, Dylan C. Erb, and Sanjay E. Sarma. 2018. A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas. IEEE Transactions on Intelligent Transportation Systems 19, 8(2018), 2391–2406.
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Pedro Wightman, Winston Coronell, Daladier Jabba, Miguel Jimeno, and Miguel Labrador. 2011. Evaluation of location obfuscation techniques for privacy in location based information systems. In 2011 IEEE Third Latin-American Conference on Communications. IEEE, 1–6.

Cited By

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  • (2024)Privacy-Preserving Algorithm for APPs in Vehicle Intelligent Terminal System: A Compressive MethodIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344516325:11(17352-17365)Online publication date: Nov-2024
  • (2023)Privacy in Connected Vehicles: Perspectives of Drivers and Car ManufacturersService-Oriented Computing10.1007/978-3-031-45728-9_4(59-68)Online publication date: 12-Oct-2023

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IoT '22: Proceedings of the 12th International Conference on the Internet of Things
November 2022
259 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 January 2023

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Author Tags

  1. Connected Vehicle
  2. Privacy-Preserving
  3. Situation-Awareness

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IoT 2022

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Overall Acceptance Rate 28 of 84 submissions, 33%

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View all
  • (2024)Privacy-Preserving Algorithm for APPs in Vehicle Intelligent Terminal System: A Compressive MethodIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344516325:11(17352-17365)Online publication date: Nov-2024
  • (2023)Privacy in Connected Vehicles: Perspectives of Drivers and Car ManufacturersService-Oriented Computing10.1007/978-3-031-45728-9_4(59-68)Online publication date: 12-Oct-2023

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