Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing

Löbner, S.; Tronnier, F.; Pape, S. and Rannenberg, K.

In CSCS '21: ACM Computer Science in Cars Symposium, Ingolstadt, Germany, November 30th, 2021, pages 7:1-7:11, ACM, 2021.

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.

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Bibtex

@InProceedings{LTPR21cscs,
  author    = {Sascha L{\"o}bner and Fr{\'{e}}d{\'{e}}ric Tronnier and Sebastian Pape and Kai Rannenberg},
  title     = {Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing},
  booktitle = {{{CSCS} '21: ACM Computer Science in Cars Symposium, Ingolstadt, Germany, November 30th, 2021}},
  year      = {2021},
  editor    = {Bj{\"{o}}rn Br{\"{u}}cher and Christoph Krau{\ss} and Mario Fritz and Hans{-}Joachim Hof and Oliver Wasenm{\"{u}}ller},
  pages     = {7:1--7:11},
  month     = {11},
  publisher = {ACM},
  doi       = {https://doi.org/10.1145/3488904.3493380},
  keywords  = {privacy, PETs, IoT},
  url       = {https://dl.acm.org/doi/10.1145/3488904.3493380},
}

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