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Article type: Research Article
Authors: Belhadri, Abdelkader* | Benyettou, Mohammed
Affiliations: Département d’informatique/Laboratoire LAMOSI, Université des Sciences et de la Technologie d’Oran-Mohamed-Boudiaf, Oran 31000, Algérie
Correspondence: [*] Corresponding author: Abdelkader Belhadri, Département d’informatique/Laboratoire LAMOSI, Université des Sciences et de la Technologie d’Oran-Mohamed-Boudiaf, USTO-MB, BP 1505, El M’naouar, Oran 31000, Algérie. Tel.: +213 670122976; E-mail: abdelkader.belhadri@univ-usto.dz.
Abstract: Ensuring protection of sensitive data in embedded devices widespread and operanting in adversary environment is a major issue. Among lot of instance of this problem, IoT is the well-know case study. In addition, very often for cost reason, the devices used in Internet of Things (IoT) do not integrate secure components (cryptoprocessor, encrypted memory, etc) like smart card. To solve this issue, this paper presents two contributions to fuzzy vault-biometric cryptosystems which can enable IoT devices supporting biometry to secure sensitive data they embed in their memory when they operate in an adverse environment. If an adversary captures a device and read content of this regular memory, it will be very difficult for him to recover the protected data using brute-force. The first advantage of the proposals is thus to enable IoT devices to still embed regular memory, to protect sensitive data, instead of an expensive native encrypted memory or to add a cryptoprocessor. The second advantage of the proposals is to enable to not require storing of helper data and thus improve security and also save memory. Obviously our proposals can also be used in already secure devices to enhance the security level. Experimental results performed using fingerprint modality show that the proposals have the potential to efficiently protect sensitive data despite the strong constrained of IoT devices.
Keywords: Biometric cryptosystem, fuzzy vault, internet of things, sensitive data, encrypted memory
DOI: 10.3233/MGS-180293
Journal: Multiagent and Grid Systems, vol. 14, no. 3, pp. 307-320, 2018
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