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Title: Convolutional neural network biometric cryptosystem for the protection of the blockchain's private key
Authors: Albakri, Alfaisal
Mokbel, Chafic 
Affiliations: Faculty of Engineering 
Keywords: Blockchain
Biometric cryptosystem
Convolutional neural networks
Face recognition
Issue Date: 2019-01-01
Publisher: Elsevier
Part of: Procedia Computer Science, Vol. 160
Start page: 235
End page: 240
Conference: International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2019 and International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2019 ( 10th and 9th : 4-7 Nov, 2019 : Coimbra, Portugal )
Blockchain technology has attracted a lot of attention in the previous years as a secure way to protect transactions in different processes. It has been particularly used to define cryptocurrencies. While inherently secure against classical single node attacks, the blockchain cryptocurrencies have recently been subject to attacks by malwares able to capmre a single user wallet and its included keys. In this work we propose the use of biometric crypto systems to control the access to the wallets on single machines. After a brief description of the blockchain. the cryptocurrencies and the possible attacks, the paper describes the use of convolutional neural network face recognition as a tool to extract biometric features that help in a key binding approach to protect the personal data in the wallet. Experiments have been conducted on three independent face datasets and the results obtained are satisfactory. The equal error rate between false acceptance and false rejection is negligible when testing on images from the same dataset used for the training of the convolutional neural network. This generalizes well when experimenting on two other independent datasets. These results prove that face crypto systems can be used to protect the access on sensitive data existing in the wallets of many cryptocurrencies.
DOI: 10.1016/j.procs.2019.09.462
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Computer Engineering

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