Sascha Löbner
ABSTRACT
Vehicles are becoming interconnected and autonomous while collecting, sharing and processing large amounts of personal, and private data. When developing a service that relies on such data, ensuring privacy preserving data sharing and processing is one of the main challenges. Often several entities are involved in these steps and the interested parties are manifold. To ensure data privacy, a variety of different de-identification techniques exist that all exhibit unique peculiarities to be considered. In this paper, we show at the example of a location-based service for weather prediction of an energy grid operator, how the different de-identification techniques can be evaluated. With this, we aim to provide a better understanding of state-of-the-art de-identification techniques and the pitfalls to consider by implementation. Finally, we find that the optimal technique for a specific service depends highly on the scenario specifications and requirements.
- 2014. Consumer Privacy Protection Principles – PRIVACY PRINCIPLES FOR VEHICLE TECHNOLOGIES AND SERVICES. https://cryptome.org/2014/11/auto-privacy-principles.pdfGoogle Scholar
- 2017. Vehicle Data Privacy – Industry and Federal Efforts Under Way, but NHTSA Needs to Define Its Role. https://www.gao.gov/assets/gao-17-656.pdfGoogle Scholar
- 2020. Guidelines 1/2020 on processing personal data in the context of connected vehicles and mobility related applications. https://edpb.europa.eu/sites/default/files/consultation/edpb_guidelines_202001_connectedvehicles.pdfGoogle Scholar
- Mohammad Al-Rubaie and J Morris Chang. 2019. Privacy-preserving machine learning: Threats and solutions. IEEE Security & Privacy 17, 2 (2019), 49–58.Google ScholarCross Ref
- Miguel E. Andrés, Nicolás E. Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. 2013. Geo-indistinguishability: Differential privacy for location-based systems. In Proceedings of the ACM Conference on Computer and Communications Security. https://doi.org/10.1145/2508859.2516735 arxiv:1212.1984Google ScholarDigital Library
- J. Andrew, J. Karthikeyan, and Jeffy Jebastin. 2019. Privacy Preserving Big Data Publication On Cloud Using Mondrian Anonymization Techniques and Deep Neural Networks. In 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). 722–727. https://doi.org/10.1109/ICACCS.2019.8728384Google Scholar
- Michele Bertoncello, Gianluca Camplone, Paul Gao, Hans-Werner Kaas, Detlev Mohr, Timo Möller, and Dominik Wee. 2016. Monetizing car data—new service business opportunities to create new customer benefits. McKinsey & Company (2016).Google Scholar
- Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld. 2017. PROCHLO: Strong Privacy for Analytics in the Crowd. In SOSP 2017 - Proceedings of the 26th ACM Symposium on Operating Systems Principles. https://doi.org/10.1145/3132747.3132769 arxiv:1710.00901Google ScholarDigital Library
- Christoph Buck and Riccardo Reith. 2020. Privacy on the road? Evaluating German consumers’ intention to use connected cars. International Journal of Automotive Technology and Management 20, 3(2020), 297–318.Google ScholarCross Ref
- Alexandra Campmas, Nadina Iacob, Felice Simonelli, and Hien Vu. 2021. Big Data and B2B platforms: the next big opportunity for Europe – Report on market deficiencies and regulatory barriers affecting cooperative, connected and automated mobility.Google Scholar
- Valerie Chen, Valerio Pastro, and Mariana Raykova. 2019. Secure computation for machine learning with SPDZ. arXiv preprint arXiv:1901.00329(2019).Google Scholar
- Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev. 2019. Distributed differential privacy via shuffling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-030-17653-2_13 arxiv:1808.01394Google ScholarDigital Library
- George P Corser, Huirong Fu, and Abdelnasser Banihani. 2016a. Evaluating location privacy in vehicular communications and applications. IEEE transactions on intelligent transportation systems 17, 9(2016), 2658–2667.Google ScholarDigital Library
- George P. Corser, Huirong Fu, and Abdelnasser Banihani. 2016b. Evaluating Location Privacy in Vehicular Communications and Applications. IEEE Transactions on Intelligent Transportation Systems 17, 9(2016), 2658–2667. https://doi.org/10.1109/TITS.2015.2506579Google ScholarDigital Library
- Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/11681878_14Google ScholarDigital Library
- European Parliament and Council of The European Union. 2016. REGULATION (EU) 2016/679 General Data Protection Regulation (GDPR). http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&from=DEGoogle Scholar
- Fifi Farouk, Yasmin Alkady, and Rawya Rizk. 2020. Efficient privacy-preserving scheme for location based services in vanet system. IEEE Access 8(2020), 60101–60116.Google ScholarCross Ref
- Sebastian Frank and Arjan Kuijper. 2020. Privacy by Design: Survey on Capacitive Proximity Sensing as System of Choice for Driver Vehicle Interfaces. In Computer Science in Cars Symposium. 1–9.Google ScholarDigital Library
- Michael Gardiner, Alexander Truskovsky, George Neville-Neil, and Atefeh Mashatan. 2021. Quantum-safe Trust for Vehicles: The race is already on. Queue 19, 2 (2021), 93–115.Google ScholarDigital Library
- Marco Gruteser and Dirk Grunwald. 2003. Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the 1st international conference on Mobile systems, applications and services. 31–42.Google ScholarDigital Library
- ISO/IEC 20889:2018. 2018. Privacy enhancing data de- identification terminology and classification of techniques. INTERNATIONAL STANDARD(2018).Google Scholar
- Ioannis Krontiris, Kalliroi Grammenou, Kalliopi Terzidou, Marina Zacharopoulou, Marina Tsikintikou, Foteini Baladima, Chrysi Sakellari, and Konstantinos Kaouras. 2020. Autonomous Vehicles: Data Protection and Ethical Considerations. In Computer Science in Cars Symposium. 1–10.Google Scholar
- John Krumm. 2007. Inference attacks on location tracks. In International Conference on Pervasive Computing. Springer, 127–143.Google ScholarCross Ref
- Atul Kumar, Manasi Gyanchandani, and Priyank Jain. 2018. A comparative review of privacy preservation techniques in data publishing. In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 1027–1032.Google ScholarCross Ref
- Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine(2020). https://doi.org/10.1109/MSP.2020.2975749 arxiv:1908.07873Google Scholar
- Yi Liu, James J.Q. Yu, Jiawen Kang, Dusit Niyato, and Shuyu Zhang. 2020. Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach. IEEE Internet of Things Journal(2020). https://doi.org/10.1109/JIOT.2020.2991401 arxiv:2003.08725Google Scholar
- Abdul Majeed and Sungchang Lee. 2020. Anonymization techniques for privacy preserving data publishing: A comprehensive survey. IEEE Access (2020).Google Scholar
- Suntherasvaran Murthy, Asmidar Abu Bakar, Fiza Abdul Rahim, and Ramona Ramli. 2019. A comparative study of data anonymization techniques. In 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 306–309.Google Scholar
- Sebastian Pape and Kai Rannenberg. 2019. Applying Privacy Patterns to the Internet of Things’ (IoT) Architecture. Mobile Networks and Applications (MONET) – The Journal of SPECIAL ISSUES on Mobility of Systems, Users, Data and Computing 24, 3 (06 2019), 925–933. https://doi.org/10.1007/s11036-018-1148-2Google ScholarDigital Library
- Mert D Pesé and Kang G Shin. 2019. Survey of Automotive Privacy Regulations and Privacy-Related Attacks. (2019).Google Scholar
- Gunasekaran Raja, Sudha Anbalagan, Geetha Vijayaraghavan, Priyanka Dhanasekaran, Yasser D. Al-Otaibi, and Ali Kashif Bashir. 2020. Energy-Efficient End-to-End Security for Software Defined Vehicular Networks. IEEE Transactions on Industrial Informatics 3203, c (2020), 1–1. https://doi.org/10.1109/tii.2020.3012166Google Scholar
- Kai Rannenberg, Sebastian Pape, Frederic Tronnier, and Sascha Löbner. 2021. Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector. Technical Report. Goethe University Frankfurt. https://doi.org/10.21248/gups.63413Google Scholar
- P Ram Mohan Rao, S Murali Krishna, and AP Siva Kumar. 2018. Privacy preservation techniques in big data analytics: a survey. Journal of Big Data 5, 1 (2018), 1–12.Google Scholar
- Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, and Anderson CA Nascimento. 2019. Privacy-preserving classification of personal text messages with secure multi-party computation: An application to hate-speech detection. arXiv preprint arXiv:1906.02325(2019).Google Scholar
- Slobodan Ribaric, Aladdin Ariyaeeinia, and Nikola Pavesic. 2016. De-identification for privacy protection in multimedia content: A survey. Signal Processing: Image Communication 47 (2016), 131–151.Google ScholarDigital Library
- Rhea C Rinaldo and Timo F Horeis. 2020. A Hybrid Model for Safety and Security Assessment of Autonomous Vehicles. In Computer Science in Cars Symposium. 1–10.Google ScholarDigital Library
- Christian Roth, Sebastian Aringer, Johannes Petersen, and Mirja Nitschke. 2020. Are sensor-based business models a threat to privacy? the case of pay-how-you-drive insurance models. In International Conference on Trust and Privacy in Digital Business. Springer, 75–85.Google ScholarDigital Library
- P Samarati and L Sweeney. 1998. Protecting Privacy when Disclosing Information: k-Anonymity and its Enforcement Through Generalization and Suppresion.Proc of the IEEE Symposium on Research in Security and Privacy (1998).Google Scholar
- Yuris Mulya Saputra, DInh Thai Hoang, DIep N. Nguyen, Eryk Dutkiewicz, Markus Dominik Mueck, and Srikathyayani Srikanteswara. 2019. Energy demand prediction with federated learning for electric vehicle networks. In 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings. https://doi.org/10.1109/GLOBECOM38437.2019.9013587Google ScholarDigital Library
- Andreas Tomandl, Florian Scheuer, and Hannes Federrath. 2012. Simulation-based evaluation of techniques for privacy protection in VANETs. In 2012 IEEE 8th international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, 165–172.Google ScholarDigital Library
- Jinbao Wang, Zhipeng Cai, and Jiguo Yu. 2019. Achieving personalized k-Anonymity-Based content privacy for autonomous vehicles in CPS. IEEE Transactions on Industrial Informatics 16, 6 (2019), 4242–4251.Google ScholarCross Ref
- Jinbao Wang, Zhipeng Cai, and Jiguo Yu. 2020. Achieving Personalized k-Anonymity-Based Content Privacy for Autonomous Vehicles in CPS. IEEE Transactions on Industrial Informatics 16, 6 (2020), 4242–4251. https://doi.org/10.1109/TII.2019.2950057Google ScholarCross Ref
- Marius Wernke, Pavel Skvortsov, Frank Dürr, and Kurt Rothermel. 2014. A classification of location privacy attacks and approaches. Personal and ubiquitous computing 18, 1 (2014), 163–175.Google Scholar
- Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (2019). https://doi.org/10.1145/3298981Google ScholarDigital Library
- Feng Yin, Zhidi Lin, Yue Xu, Qinglei Kong, Deshi Li, Sergios Theodoridis, and Shuguang Cui. 2020. FEDLOC: Federated learning framework for data-driven cooperative localization and location data processing. https://doi.org/10.1109/ojsp.2020.3036276 arxiv:2003.03697Google Scholar
- Liane Yvkoff. 2020. The Success Of Autonomous Vehicles Hinges On Smart Cities. Inrix Is Making It Easier To Build Them. Forbes. https://www.forbes.com/sites/lianeyvkoff/2020/10/28/the-success-of-autonomous-vehicles-hinges-on-smart-cities-inrix-is-making-it-easier-to-build-them/Google Scholar
Recommendations
Privacy Preserving Data Analysis with the Encode, Shuffle, Analyse Architecture in Vehicular Data Sharing
EICC '23: Proceedings of the 2023 European Interdisciplinary Cybersecurity ConferenceIn recent years, vehicles have become smarter, with more data being collected and analyzed. With further digitalisation, the importance of data within and around vehicles is only going to increase, allowing stakeholders to generate new and more ...
Privacy-preserving data sharing in cloud computing
Storing and sharing databases in the cloud of computers raise serious concern of individual privacy. We consider two kinds of privacy risk: presence leakage, by which the attackers can explicitly identify individuals in (or not in) the database, and ...
A Survey on Privacy Preserving Dynamic Data Publishing
Many organizations, especially small and medium business SMB enterprises require the collection and sharing of data containing personal information. The privacy of this data must be preserved before outsourcing to the commercial public. Privacy ...
Comments