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International Workshop on Data Privacy Management

International Workshop on Quantitative Aspects in Security Assurance

DPM 2016, QASA 2016: Data Privacy Management and Security Assurance pp 113–129Cite as

Searchable Encryption for Biometric Identification Revisited

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9963))

Abstract

Cryptographic primitives for searching and computing over encrypted data have proven useful in many applications. In this paper, we revisit the application of symmetric searchable encryption (SSE) to biometric identification. Our main contribution is two SSE schemes well-suited to be applied to biometric identification over encrypted data. While existing solution uses SSE with single-keyword search and highly sequential design, we use threshold conjunctive queries and parallelizable constructions. As a result, we are able to perform biometric identification over a large amount of encrypted biometric data in reasonable time. Our two SSE schemes achieve a different trade-off between security and efficiency. The first scheme is more efficient, but is proved secure only against non-adaptive adversaries while the second is proved secure against adaptive adversaries.

G. Amchyaa—Part of this work was done while this author was an intern at Safran Identity and Security, and a student at Eurécom, Sophia-Antipolis, France.

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Acknowledgement

This work has been partially funded by the French ANR-12-CORD-0014 project SECULAR and the European H2020 TREDISEC project under the Grant Agreement 644412.

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Authors and Affiliations

  1. Safran Identity and Security, Issy-Les-Moulineaux, France

    Ghassane Amchyaa, Julien Bringer & Roch Lescuyer

Authors
  1. Ghassane Amchyaa
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  2. Julien Bringer
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  3. Roch Lescuyer
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Corresponding author

Correspondence to Ghassane Amchyaa .

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy

    Giovanni Livraga

  2. University of Skövde , Skövde, Sweden

    Vicenç Torra

  3. University of Urbino , Urbino, Italy

    Alessandro Aldini

  4. IIT National Research Council CNR , Pisa, Italy

    Fabio Martinelli

  5. Technische Universität Darmstadt , Darmstadt, Germany

    Neeraj Suri

Appendix

Appendix

Fig. 4.
figure 4

Completion of Bloom filters in the non-adaptive simulator

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Amchyaa, G., Bringer, J., Lescuyer, R. (2016). Searchable Encryption for Biometric Identification Revisited. In: Livraga, G., Torra, V., Aldini, A., Martinelli, F., Suri, N. (eds) Data Privacy Management and Security Assurance. DPM QASA 2016 2016. Lecture Notes in Computer Science(), vol 9963. Springer, Cham. https://doi.org/10.1007/978-3-319-47072-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-47072-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47071-9

  • Online ISBN: 978-3-319-47072-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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