Title

Securing cloud storage by transparent biometric cryptography

Document Type

Conference Proceeding

Publisher

Springer Verlag

School

Security Research Institute

Comments

Originally published as: Abed, L., Clarke, N., Ghita, B., & Alruban, A. (2018, November). Securing Cloud Storage by Transparent Biometric Cryptography. In International Conference on Security for Information Technology and Communications (pp. 97-108). Springer, Cham. Original paper available here.

Abstract

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Given weak passwords, malicious attacks have been happened across a variety of well-known storage services (e.g. Dropbox and Google Drive) – resulting in loss the confidentiality. Although today’s use of third-party cryptographic applications can independently encrypt data, it is arguably cumbersome to manually cipher/decipher each file and administer many keys. Biometric key generation can produce robust keys replacing the need to recall them. However, it still poses usability issues in terms of having to present biometric credentials each time a file needs to be encrypted/decrypted. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. This paper investigates the feasibility of key generation on the fly via transparent modalities including fingerprint, face and keystrokes. Sets of experiments using functional datasets reflecting a transparent fashion are conducted to determine the reliability of creating a 256-bit key via pattern classification. Practically, the proposed approach needs to create the correct key once a minute. In view of collecting numerous samples transparently, it is possible then to trade-off the false rejection against the false acceptance to tackle the high error. Accordingly, the average FAR was 0.9%, 0.02%, and 0.06% for fingerprint, face, and keystrokes respectively. © Springer Nature Switzerland AG 2019.

DOI

10.1007/978-3-030-12942-2_9

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