Debiasing of SRAM PUFs: selection and balancing

Lieneke Kusters; Frans M.J. Willems

doi:10.1109/WIFS47025.2019.9035094

Debiasing of SRAM PUFs: selection and balancing

Lieneke Kusters, Frans M.J. Willems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

Fuzzy commitment is used to bind a secret key to an SRAM-PUF observation vector. The fuzzy commitment scheme is secure as long as the observation vector has full entropy. Here, we assume that the observation vectors are biased, and explore two elementary schemes for debiasing: Selection and balancing. We study the performance of the schemes from an information theoretic perspective.

Original language	English
Title of host publication	2019 IEEE International Workshop on Information Forensics and Security, WIFS 2019
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Electronic)	9781728132174
DOIs	https://doi.org/10.1109/WIFS47025.2019.9035094
Publication status	Published - Dec 2019
Event	2019 IEEE International Workshop on Information Forensics and Security, WIFS 2019 - Delft, Netherlands Duration: 9 Dec 2019 → 12 Dec 2019

Conference

Conference	2019 IEEE International Workshop on Information Forensics and Security, WIFS 2019
Country/Territory	Netherlands
City	Delft
Period	9/12/19 → 12/12/19

Funding

This work was funded by Eurostars-2 joint programme with co-funding from the EU Horizon 2020 programme under the E! 11897 RESCURE project.

Access to Document

10.1109/WIFS47025.2019.9035094

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title = "Debiasing of SRAM PUFs: selection and balancing",

abstract = "Fuzzy commitment is used to bind a secret key to an SRAM-PUF observation vector. The fuzzy commitment scheme is secure as long as the observation vector has full entropy. Here, we assume that the observation vectors are biased, and explore two elementary schemes for debiasing: Selection and balancing. We study the performance of the schemes from an information theoretic perspective.",

author = "Lieneke Kusters and Willems, {Frans M.J.}",

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Kusters, L & Willems, FMJ 2019, Debiasing of SRAM PUFs: selection and balancing. in 2019 IEEE International Workshop on Information Forensics and Security, WIFS 2019., 9035094, Institute of Electrical and Electronics Engineers, Piscataway, 2019 IEEE International Workshop on Information Forensics and Security, WIFS 2019, Delft, Netherlands, 9/12/19. https://doi.org/10.1109/WIFS47025.2019.9035094

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AU - Willems, Frans M.J.

PY - 2019/12

Y1 - 2019/12

N2 - Fuzzy commitment is used to bind a secret key to an SRAM-PUF observation vector. The fuzzy commitment scheme is secure as long as the observation vector has full entropy. Here, we assume that the observation vectors are biased, and explore two elementary schemes for debiasing: Selection and balancing. We study the performance of the schemes from an information theoretic perspective.

AB - Fuzzy commitment is used to bind a secret key to an SRAM-PUF observation vector. The fuzzy commitment scheme is secure as long as the observation vector has full entropy. Here, we assume that the observation vectors are biased, and explore two elementary schemes for debiasing: Selection and balancing. We study the performance of the schemes from an information theoretic perspective.

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PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

Y2 - 9 December 2019 through 12 December 2019

ER -

Debiasing of SRAM PUFs: selection and balancing

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