Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/123083
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Type: Journal article
Title: Lightweight (reverse) fuzzy extractor with multiple reference PUF responses
Author: Gao, Y.
Su, Y.
Xu, L.
Ranasinghe, D.C.
Citation: IEEE Transactions on Information Forensics and Security, 2019; 14(7):1887-1901
Publisher: IEEE
Issue Date: 2019
ISSN: 1556-6013
1556-6021
Statement of
Responsibility: 
Yansong Gao, Yang Su, Lei Xu and Damith C. Ranasinghe
Abstract: A physical unclonable function (PUF), like a fingerprint, exploits manufacturing randomness to endow each physical item with a unique identifier. One primary PUF application is the secure derivation of volatile cryptographic keys using a fuzzy extractor (FE) comprising: 1) a secure sketch and 2) an entropy extractor. Although the entropy extractor can be lightweight, the overhead of the secure sketch responsible for correcting naturally noisy PUF responses is usually high. We observe that, in general, response unreliability with respect to an enrolled reference measurement increases with increasing differences between the in-the-field PUF operating condition and the operating condition used in evaluating the enrolled reference response. For the first time, we exploit such an inadvertent but important observation. In contrast to the conventional single reference response enrollment, we propose enrolling multiple reference responses (MRRs) subject to the same challenge but under multiple distinct operating conditions. The critical observation here is that one of the reference operating conditions is likely to be closer to the operating condition of the field deployed PUF, thus resulting in minimizing the expected unreliability when compared to the single reference under the nominal condition. As a consequence, MRR greatly reduces the demand for the expected number of erroneous bits requiring correction and, subsequently, achieves a significant reduction in the error correction overhead. The significant implementation efficiency gains from the proposed MRR method are demonstrated from software implementations of FEs on batteryless resource constraint computational radio frequency identification devices, where realistic PUF data are collected from intrinsic static random access memory PUFs.
Keywords: Physical unclonable functions; key generation; reverse fuzzy extractor; fuzzy extractor; lightweight authentication; computational radio frequency identification
Rights: © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
DOI: 10.1109/TIFS.2018.2886624
Grant ID: http://purl.org/au-research/grants/arc/DP140103448
Published version: http://dx.doi.org/10.1109/tifs.2018.2886624
Appears in Collections:Aurora harvest 4
Computer Science publications

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