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What Is the Cost of Privacy?

  • Conference paper
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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1602))

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Abstract

Grade research has to be replicable, thus the used data need to be publicly available. Speaking, e.g., about object detection task, where image data for autonomous driving also contain privacy information such as faces and license plates, the publication of data may be harmful to captured people. The solution to the moral dilemma is to anonymize the data. In this study, our aim is to investigate the effect of various anonymization techniques on the performance of algorithms that use such data. We discuss anonymization methods that remove and replace privacy data and select three methods to replace the privacy data: blurring, permutation, and replacing the area with a constant value. We adopted the Cityscapes dataset from which we extracted areas containing privacy information and are the manner of the anonymization methods. Our benchmark involves three famous object detectors: YOLOv3, Mask R-CNN with ResNet-50 backbone, and Mask R-CNN with Swin-T backbone. The results show that the impact of anonymization methods on the performance is negligible and the impact is similar for both convolutional-based and transformer-based backbones. Since the anonymization did not impact the model’s performance, we would recommend to anonymize datasets of this kind to prevent potential GDPR issues.

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Notes

  1. 1.

    https://gitlab.com/irafm-ai/ipmu-2022-anonymization.

  2. 2.

    paperswithcode.com/sota/object-detection-on-coco.

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Acknowledgement

The work is partially supported by grant SGS17/PřF-MF/2022.

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Authors and Affiliations

  1. Centre of Excellence IT4Innovations, Institute for Research and Applications of Fuzzy Modeling. 30. Dubna 22, University of Ostrava, Ostrava, Czech Republic

    Petr Dvořáček & Petr Hurtik

Authors
  1. Petr Dvořáček
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  2. Petr Hurtik
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Corresponding author

Correspondence to Petr Dvořáček .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

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Dvořáček, P., Hurtik, P. (2022). What Is the Cost of Privacy?. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_55

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  • DOI: https://doi.org/10.1007/978-3-031-08974-9_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08973-2

  • Online ISBN: 978-3-031-08974-9

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