Skip to main content
SpringerLink
Log in

Smart Privacy Visor: Bridging the Privacy Gap

  • Conference paper
  • First Online:
Business Information Systems Workshops (BIS 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 228))

Included in the following conference series:

Abstract

Due to the propagation of devices with imaging capabilities, the amount of pictures taken in public spaces has risen. Due to this, unintentionally photographed bystanders are often represented in pictures without being aware of it. Social networks and search engines make these images easier accessible due to the available meta-data and the tagging and linking functionality provided by these services. Facial recognition amplifies the privacy implications for the individuals in these pictures. Overall there exist three main classes of wearable picture-related Privacy Enhancing Technologies (PETs). As they need different prerequisites to operate and become effective they have unique time frames in the future where they can be effective even if introduced today. The group of face pattern destroying picture PETs work directly against current face detection algorithms and is the choice for immediate usage. These PETs destroy face patterns and inhibit the detection and automated processing and meta-data enrichment of individuals. This unconditionally visual destructive behavior can be a major obstacle in transition to other PETs. In this paper, we describe how to master a smooth transition between these classes including the restoration of the visual damage some of these methods entail. Furthermore, we propose the Smart Privacy Visor, a PET which combines the previously published Privacy Visor and the Picture Privacy Policy Framework. The overall goal of this transition is to create a PET that avoids identifiable and linkable properties which contradicts the goals of picture PETs in the first place and offer a visually appealing photographic result at the same time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Harvey, A.: CV Dazzle (2010–2012) http://cvdazzle.com/ and http://ahprojects.com/projects/cv-dazzle. Accessed 2 November 2014

  2. Yamada, T., Gohshi, S., Echizen, I.: Use of invisible noise signals to prevent privacy invasion through face recognition from camera images. In: Proceedings of the 20th ACM International Conference on Multimedia, MM 2012, pp. 1315–1316. ACM (2012)

    Google Scholar 

  3. Yamada, T., Gohshi, S., Echizen, I.: Privacy visor: method for preventing face image detection by using differences in human and device sensitivity. In: De Decker, B., Dittmann, J., Kraetzer, C., Vielhauer, C. (eds.) CMS 2013. LNCS, vol. 8099, pp. 152–161. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Yamada, T., Gohshi, S., Echizen, I.: Privacy visor: method based on light absorbing and reflecting properties for preventing face image detection. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (2013)

    Google Scholar 

  5. Patel, S.N., Summet, J.W., Truong, K.N.: Blindspot: creating capture-resistant spaces. In: Senior, A. (ed.) Protecting Privacy in Video Surveillance, pp. 185–201. Springer, London (2009)

    Chapter  Google Scholar 

  6. Schiff, J., Meingast, M., Mulligan, D.K., Sastry, S., Goldberg, K.: Respectful cameras: detecting visual markers in real-time to address privacy concerns. In: Senior, A. (ed.) Protecting Privacy in Video Surveillance, pp. 65–89. Springer, London (2009)

    Chapter  Google Scholar 

  7. Dabrowski, A., Weippl, E.R., Echizen, I.: Framework based on privacy policy hiding for preventing unauthorized face image processing. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (2013)

    Google Scholar 

  8. Pallas, F., Ulbricht, M.R., Jaume-Palasí, L., Höppner, U.: Offlinetags: a novel privacy approach to online photo sharing. In: CHI 2014 Extended Abstracts on Human Factors in Computing Systems, pp. 2179–2184. ACM (2014)

    Google Scholar 

  9. Barhm, M.S., Qwasmi, N., Qureshi, F.Z., El-Khatib, K.: Negotiating privacy preferences in video surveillance systems. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part II. LNCS, vol. 6704, pp. 511–521. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Henne, B., Szongott, C., Smith, M.: Snapme if you can: privacy threats of other peoples’ geo-tagged media and what we can do about it. In: Proceedings of the Sixth ACM Conference on Security and Privacy in Wireless and Mobile Networks, pp. 95–106. ACM (2013)

    Google Scholar 

  11. Brassil, J.: Using mobile communications to assert privacy from video surveillance. In: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, p. 8. IEEE (2005)

    Google Scholar 

  12. Yus, R., Pappachan, P., Das, P.K., Mena, E., Joshi, A., Finin, T.: Demo: Faceblock: privacy-aware pictures for google glass. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services. ACM (2014)

    Google Scholar 

  13. Wickramasuriya, J., Datt, M., Mehrotra, S., Venkatasubramanian, N.: Privacy protecting data collection in media spaces. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, pp. 48–55. ACM (2004)

    Google Scholar 

  14. Chinomi, K., Nitta, N., Ito, Y., Babaguchi, N.: PriSurv: privacy protected video surveillance system using adaptive visual abstraction. In: Satoh, S., Nack, F., Etoh, M. (eds.) MMM 2008. LNCS, vol. 4903, pp. 144–154. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Moon, H.M., Seo, C.H., Chung, Y., Pan, S.B.: Privacy protection technology in video surveillance system. In: 4th International Conference on Embedded and Multimedia Computing, EM-Com 2009, pp. 1–6. IEEE (2009)

    Google Scholar 

  16. Ashok, A., Nguyen, V., Gruteser, M., Mandayam, N., Yuan, W., Dana, K.: Do not share! invisible light beacons for signaling preferences to privacy-respecting cameras. In: Proceedings of the 1st ACM MobiCom Workshop on Visible Light Communication Systems, pp. 39–44. ACM (2014)

    Google Scholar 

  17. Koster, M.: A Standard for Robot Exclusion. http://www.robotstxt.org/orig.html. Accessed 15 July 2015

  18. Lin, D., Tang, X.: Quality-driven face occlusion detection and recovery. In: Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–7. IEEE (2007)

    Google Scholar 

  19. Mo, Z., Lewis, J.P., Neumann, U.: Face inpainting with local linear representations. In: BMVC, pp. 1–10 (2004)

    Google Scholar 

  20. Hwang, B.-W., Lee, S.-W.: Reconstructing a whole face image from a partially damaged or occluded image by multiple matching. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 692–701. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  21. Jimenez, A.E., Dabrowski, A., Sonehara, N., Martinez, J.M.M., Echizen, I.: Tag detection for preventing unauthorized face image processing. In: Shi, Y.-Q., Kim, H.J., Pérez-González, F., Yang, C.-N. (eds.) IWDW 2014. LNCS, vol. 9023, pp. 513–524. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

Download references

Acknowledgment

This work is supported by the National Institute of Informatics’ internship program, a netidee grant by the Internet Foundation Austria (IPA), and the Comet K1 program of the Austrian Research Promotion Agency (FFG).

Author information

Authors and Affiliations

  1. SBA Research, Vienna, Austria

    Adrian Dabrowski, Katharina Krombholz & Edgar R. Weippl

  2. National Institute of Informatics, Tokyo, Japan

    Isao Echizen

Authors
  1. Adrian Dabrowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katharina Krombholz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edgar R. Weippl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Isao Echizen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrian Dabrowski .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Witold Abramowicz

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Dabrowski, A., Krombholz, K., Weippl, E.R., Echizen, I. (2015). Smart Privacy Visor: Bridging the Privacy Gap. In: Abramowicz, W. (eds) Business Information Systems Workshops. BIS 2015. Lecture Notes in Business Information Processing, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-319-26762-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26762-3_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26761-6

  • Online ISBN: 978-3-319-26762-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation