Title: A Privacy-Preserving Model for Biometric Fusion,
Authors: Toli, Christina-Angeliki ×
Aly, Abdelrahaman #
Preneel, Bart #
Issue Date: 28-Oct-2016
Publisher: Springer-Verlag
Host Document: Lecture Notes in Computer Science vol:10052 pages:743-748
Series Title: Cryptology and Network Security
Conference: CANS edition:15 location:Milan, Italy date:14-16 November 2016
Abstract: Biometric designs have attracted attention in practical technological schemes with high requirements in terms of accuracy, security and privacy. Nevertheless, multimodalities have been approached with skepticism, as fusion deployments are affected by performance metrics. In this paper, we introduce a basic fusion model blueprint for a privacy-preserving cloud-based user verification/authentication. We consider the case of three modalities, permanently "located" in different databases of semi-honest providers, being combined according to their strength performance parameters, in a user-specific weighted score level fusion. Secure multiparty computation techniques are utilized for protecting confidentiality and privacy among the parties.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:ESAT - COSIC, Computer Security and Industrial Cryptography (+)
× corresponding author
# (joint) last author

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