A Privacy-Preserving Model for Biometric Fusion View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016-10-28

AUTHORS

Christina-Angeliki Toli , Abdelrahaman Aly , Bart Preneel

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. More... »

PAGES

743-748

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-48965-0_54

DOI

http://dx.doi.org/10.1007/978-3-319-48965-0_54

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085991972


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