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Title:

Machine Learning of Physical Unclonable Functions using Helper Data: Revealing a Pitfall in the Fuzzy Commitment Scheme

Document type:
Zeitschriftenaufsatz
Author(s):
Strieder, Emanuele and Frisch, Christoph and Pehl, Michael
Abstract:
Physical Unclonable Functions (PUFs) are used in various key-generation schemes and protocols. Such schemes are deemed to be secure even for PUFs with challenge-response behavior, as long as no responses and no reliability information about the PUF are exposed. This work, however, reveals a pitfall in these constructions: When using state-of-the-art helper data algorithms to correct noisy PUF responses, an attacker can exploit the publicly accessible helper data and challenges. We show that wit...     »
Keywords:
Physical Unclonable Function · PUF · Machine Learning · Supervised Learning · Fuzzy Commitment Scheme · Fuzzy Extractor · Error Correcting Code · Neural Network · Key Storage · Key Distribution
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
IACR Transactions on Cryptographic Hardware andEmbedded Systems
Year:
2021
Journal volume:
2021
Year / month:
2021-02
Quarter:
1. Quartal
Month:
Feb
Journal issue:
2
Pages contribution:
1–36
Fulltext / DOI:
doi:10.46586/tches.v2021.i2.1-36
WWW:
https://tches.iacr.org/index.php/TCHES/article/view/8786
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