Author(s) :

Bala Kumar R. S , Sujitha R

Conference Name :

International Conference on scientific innovations in Science, Technology, and Management (NGCESl-2023)

Abstract :

Benefited from its reliability and convenience, biometric identification has become one of the
most popular authentication technologies. Due to the sensitivity of biometric data, various
privacy-preserving biometric identification protocols have been proposed. However, the low
computational efficiency or the security vulnerabilities of these protocols limit their wide
deployment in practice. To further improve the efficiency and enhance the security, in this
project, propose two new privacy-preserving biometric identification outsourcing protocols.
One mainly utilizes the efficient Householder transformation and permutation technique to
realize the high-efficiency intention under the known candidate attack model. The other
initializes a novel random split technique and combines it with the invertible linear
transformation to achieve a higher security requirement under the known-plaintext attack
model. Also, argue the security of our proposed two protocols with a strict theoretical analysis
and, by comparing them with the prior existing works, comprehensively evaluate their
efficiency.

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