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https://doi.org/10.2498/cit.1002764

Hardware Accelerator Approach Towards Efficient Biometric Cryptosystems for Network Security

Charles McGuffey ; Clarkson University, Potsdam, New York, USA
Chen Liu orcid id orcid.org/0000-0003-1558-6836 ; Clarkson University, Potsdam, New York, USA
Stephanie Schuckers ; Clarkson University, Potsdam, New York, USA


Puni tekst: engleski pdf 688 Kb

str. 329-340

preuzimanja: 540

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Sažetak

Protecting data and its communication is a critical part of the modern network. The science of protecting data, known as cryptography, uses secret keys to encrypt data in a format that is not easily decipherable. However, most commonly secure logons for a workstation connected to a network use passwords to perform user authentication. These passwords are a weak link in the security chain, and are a common point of attack on cryptography schemes. One alternative to password usage for network security is to use a person’s physical characteristics to verify who the person is and unlock the data correspondingly. This study focuses on the Cambridge biometric cryptosystem, a system for performing user authentication based on a user’s iris data. The implementation of this system expanded from a single-core software-only system to a collaborative system consisting of a single core and a hardware accelerator. The experiment takes place on a Xilinx Zynq-7000 All Programmable SoC. Software implementation is performed on one of the embedded ARM A9 cores while hardware implementation makes use of the programmable logic. Our hardware acceleration produced a speedup of 2.2X while reducing energy usage to 47.5 % of its original value for the combined enrolment and verification process. These results are also compared to a many-core acceleration of the same system, providing an analysis of different acceleration methods.

Ključne riječi

hardware acceleration; biometric cryptosystem; iris recognition; Reed-Solomon code; Hadamard code

Hrčak ID:

149738

URI

https://hrcak.srce.hr/149738

Datum izdavanja:

18.12.2015.

Posjeta: 1.103 *