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Document type:
Zeitschriftenaufsatz 
Author(s):
Strieder, Emanuele and Frisch, Christoph and Pehl, Michael 
Title:
Machine Learning of Physical Unclonable Functions using Helper Data: Revealing a Pitfall in the Fuzzy Commitment Scheme 
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:
Pages contribution:
1–36