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You’ve Got Nothing on Me! Privacy Friendly Face Recognition Reloaded

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Computer Security (ESORICS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12580))

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Abstract

Nowadays, almost anyone can take pictures at any time. Simultaneously, services such as social networks make it easy to share and redistribute these images. Users who do not want pictures of them to be recorded and distributed can hardly defend themselves against this. With the introduction of the GDPR in the European Union, users can now at least demand the deletion of such unsolicited uploaded data from web platforms. To find such images, however, the user must first upload comparative images to such a web service so that this service can compare them with its database to show the user whether unwanted images exist or not. This means that the user must involuntarily pass on his biometric data to a web service where he does not actually want his data to be saved. Thus, in this paper, we present our privacy-friendly face recognition approach based on Local Binary Patterns and Error Correction Codes, that allows users to query web services for the presence of unwanted images without revealing biometric information. We evaluated each step of our approach with the “FERET database of facial images” and the “Yale Face Database”.

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Notes

  1. 1.

    “Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office”.

  2. 2.

    Resulting in an evenly distributed baseline of \(50\%\) true cases and \(50\%\) false cases.

  3. 3.

    Resulting in a baseline of \({\approx }{61.11}\%\) true cases and \({\approx }{38.89}\%\) false cases.

  4. 4.

    https://github.com/cburkert/fuzzy-commitment.

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Author information

Authors and Affiliations

  1. TU Dresden, Dresden, Germany

    Stephan Escher, Patrick Teufert, Lukas Hain & Thorsten Strufe

Authors
  1. Stephan Escher
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  2. Patrick Teufert
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  3. Lukas Hain
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  4. Thorsten Strufe
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Corresponding author

Correspondence to Stephan Escher .

Editor information

Editors and Affiliations

  1. University of Surrey, Guildford, UK

    Ioana Boureanu

  2. University of Surrey, Guildford, UK

    Constantin Cătălin Drăgan

  3. University of Surrey, Guildford, UK

    Mark Manulis

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Thanassis Giannetsos

  5. Ionian University, Kerkira, Greece

    Christoforos Dadoyan

  6. UBITECH Ltd., Chalandri, Greece

    Panagiotis Gouvas

  7. Dartmouth College and Naval Information Warfare Center, Hanover, NH, USA

    Roger A. Hallman

  8. University of Kent, Canterbury, Kent, UK

    Shujun Li

  9. Teessside University, Middlesbrough, UK

    Victor Chang

  10. Technical University of Berlin, Berlin, Germany

    Frank Pallas

  11. Alexander von Humboldt Institute for Internet and Society, Berlin, Germany

    Jörg Pohle

  12. Ruhr University Bochum, Bochum, Germany

    Angela Sasse

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Escher, S., Teufert, P., Hain, L., Strufe, T. (2020). You’ve Got Nothing on Me! Privacy Friendly Face Recognition Reloaded. In: Boureanu, I., et al. Computer Security. ESORICS 2020. Lecture Notes in Computer Science(), vol 12580. Springer, Cham. https://doi.org/10.1007/978-3-030-66504-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-66504-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66503-6

  • Online ISBN: 978-3-030-66504-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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