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Privacy Dashcam – Towards Lawful Use of Dashcams Through Enforcement of External Anonymization

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Data Privacy Management, Cryptocurrencies and Blockchain Technology (DPM 2017, CBT 2017)

Abstract

Dashcams are small, dashboard mounted camera systems that continuously monitor the area around a vehicle and record video images on a portable storage device. According to many data protection authorities, dashcams constitute surveillance systems that are operated by private individuals in public places. By continuously acquiring personal data they interfere disproportionately with the right of informational self-determination. One approach to make dashcams compliant to data protection law is to automatically identify personal information – at least pedestrian’s faces and license plates – in the captured video image and subsequently disguise them. Even though appropriate anonymization methods exist, high computational costs prevent their use in portable dashcams. This article presents a new approach that enforces the anonymization of encrypted dashcam videos on a dedicated computer system, before the user gets access to the videos. To accomplish this, classified images are safeguarded by usage control techniques on the way from the camera to the anonymization component. By applying the developed system, any existing dashcam can ultimately be enhanced by privacy protection capabilities.

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Notes

  1. 1.

    See Bretthauer/Krempel/Birnstill, CR 2015, 239 (242) [3].

  2. 2.

    E. g. § 6 b BDSG, § 50 a ff. ÖDSG, §§ 16 ff. Data Protection Act, Lithuania, § 26 Act on Processing of Personal Data, Denmark, § 6 Data Protection Act, Liechtenstein, §§ 36 ff. Personal Data Act, Norway.

  3. 3.

    See Bretthauer/Krempel, in: Schweighofer/Kummer/Htzendorfer (ed.), Transparenz – Tagungsband des 17. Internationalen Rechtsinformatik Symposions, 2014, S. 525, 532 [2]; on the requirements laid down in Art. 52 of the Charter of Fundamental Rights of the EU see Rieckhoff, Der Vorbehalt des Gesetzes im Europarecht, 2007, p. 155 ff [13].

  4. 4.

    E. g. Ernst, CR 2015, 620 (623) [6].

  5. 5.

    https://www.swissbit.com/products/security-products/overwiev/security-products-overview/.

References

  1. Birnstill, P., Ren, D., Beyerer, J.: A user study on anonymization techniques for smart video surveillance. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2015)

    Google Scholar 

  2. Bretthauer, S., Krempel, E.: Videomonitoring zur sturzdetektion und alarmierung - eine technische und rechtliche analyse. In: Schweighofer, E., Kummer, F., Htzendorfer, W. (eds.) Transparenz - Tagungsband des 17. Internationalen Rechtsinformatik Symposions. pp. 525–534 (2014)

    Google Scholar 

  3. Bretthauer, S., Krempel, E., Birnstill, P.: Intelligente videoberwachnug in kranken- und pflegeeinrichtungen von morgen. Computer und Recht pp. 239–245 (2015)

    Google Scholar 

  4. Dufaux, F.: Video scrambling for privacy protection in video surveillance: recent results and validation framework. In: Proceeding of SPIE, vol. 8063, pp. 806302–806302-14 (2011). https://dx.doi.org/10.1117/12.883948

  5. Dufaux, F., Ebrahimi, T.: Region-based transform-domain video scrambling. In: Proceeding of SPIE, vol. 6077, pp. 60771U–60771U-9 (2006). https://dx.doi.org/10.1117/12.643048

  6. Ernst, S.: Zur un-zulssigkeit von dashcams. Computer und Recht pp. 620–624 (2015)

    Google Scholar 

  7. Harvan, M., Pretschner, A.: State-based usage control enforcement with data flow tracking using system call interposition. In: 2009 Third International Conference on Network and System Security, NSS 2009, pp. 373–380. IEEE (2009)

    Google Scholar 

  8. Hosang, J., Omran, M., Benenson, R., Schiele, B.: Taking a deeper look at pedestrians. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4073–4082 (2015)

    Google Scholar 

  9. Janard, K., Marurngsith, W.: Accelerating real-time face detection on a raspberry pi telepresence robot. In: Proceedings of the Fifth International Conference on Innovative Computing Technology, INTECH 2015, pp. 136–141 (May 2015)

    Google Scholar 

  10. Korshunov, P., Ebrahimi, T.: Using warping for privacy protection in video surveillance. In: 2013 18th International Conference on Digital Signal Processing (DSP), pp. 1–6 (July 2013)

    Google Scholar 

  11. Park, J., Sandhu, R.: Towards usage control models: Beyond traditional access control. In: Proceedings of 7th ACM Symposium on Access Control Models and Technologies (2002)

    Google Scholar 

  12. Pretschner, A., Hilty, M., Basin, D.A.: Distributed usage control. Commun. ACM 49(9), 39–44 (2006). doi:10.1145/1151053

    Article  Google Scholar 

  13. Rieckhoff, H.: Der Vorbehalt des Gesetzes im Europarecht. Mohr Siebeck, Tbingen (2007)

    Google Scholar 

  14. Rinner, B., Winkler, T.: Privacy-protecting smart cameras. In: Proceedings of the International Conference on Distributed Smart Cameras, ICDSC 2014, pp. 40:1–40:5, NY, USA. ACM, New York (2014)

    Google Scholar 

  15. Tian, Y., Luo, P., Wang, X., Tang, X.: Pedestrian detection aided by deep learning semantic tasks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5079–5087 (2015)

    Google Scholar 

  16. Wagner, P.G., Birnstill, P., Krempel, E., Bretthauer, S., Beyerer, J.: Privacy-dashcam - datenschutzfreundliche dashcams durch erzwingen externer anonymisierung. In: Informatik 2016, 46. Jahrestagung der Gesellschaft für Informatik, 26.-30. Klagenfurt, Österreich. pp. 427–440 (2016). http://subs.emis.de/LNI/Proceedings/Proceedings259/article44.html

  17. Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: How far are we from solving pedestrian detection? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1259–1267 (2016)

    Google Scholar 

  18. Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1751–1760. IEEE (2015)

    Google Scholar 

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Correspondence to Pascal Birnstill .

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Wagner, P., Birnstill, P., Krempel, E., Bretthauer, S., Beyerer, J. (2017). Privacy Dashcam – Towards Lawful Use of Dashcams Through Enforcement of External Anonymization. In: Garcia-Alfaro, J., Navarro-Arribas, G., Hartenstein, H., Herrera-Joancomartí, J. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2017 2017. Lecture Notes in Computer Science(), vol 10436. Springer, Cham. https://doi.org/10.1007/978-3-319-67816-0_11

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  • DOI: https://doi.org/10.1007/978-3-319-67816-0_11

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