Cloud-based biometrics processing for privacy-preserving identification

Changhee Hahn, Hyungjune Shin, Junbeom Hur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


With the increasing number of users enrolled, biometric identification requires more computing resources to scan all records of a database and locate the best match. As such, database owners are willing to delegate user biometric information (in encrypted state) to the cloud to enroll and identify users, while preserving privacy. Wang et al. proposed a cloud-based privacy-preserving biometric scheme, a.k.a. CloudBI, in ESORICS 2015, but their security assumption does not capture practical aspects of real world attacks. In this paper, we show how an attack enrolls fake biometric data and then manipulates them to recover encrypted an identification request in CloudBI. Next, we propose an effective security patch to CloudBI, which is secure against enrollment-level attackers. Experimental results show that the proposed security patch bring about little performance degradation to CloudBI.

Original languageEnglish
Title of host publicationICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781509047499
Publication statusPublished - 2017 Jul 26
Event9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 - Milan, Italy
Duration: 2017 Jul 42017 Jul 7

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536


Other9th International Conference on Ubiquitous and Future Networks, ICUFN 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • Biometrics
  • Cloud
  • Identification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture


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