Cryptology ePrint Archive: Report 2020/888

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

Emanuele Strieder and Christoph Frisch and Michael Pehl

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 con- structions: 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 with this public information and the knowledge of the underlying error correcting code, an attacker can break the security of the system: The redundancy in the error correcting code reveals machine learnable features and labels. Learning these features and labels results in a predictive model for the dependencies between different challenge-response pairs (CRPs) without direct access to the actual PUF response. We provide results based on simulated data of a k-SUM PUF model and an Arbiter PUF model. The analysis reveals that especially the frequently used repetition code is vulnerable: Already the observation of 800 challenges and helper data bits suffices to reduce the entropy of the key down to one bit in this case. The analysis also shows that even other linear block codes like the BCH, the Reed-Muller, or the Single Parity Check code are affected by the problem. The code dependent insights we gain from the analysis allow us to suggest mitigation strategies for the identified attack. While the shown vulnerability brings Machine Learning (ML) one step closer to realistic attacks on key-storage systems with PUFs, our analysis also allows for a better understanding and evaluation of existing approaches and protocols with PUFs. Therefore, it brings the community a step further towards a more complete leakage assessment of PUFs.

Category / Keywords: cryptographic protocols / Physical Unclonable Function, PUF, Machine Learning, Supervised Learning, Fuzzy Commitment Scheme, Fuzzy Extractor, Error Correcting Code, Neural Network, Key Storage, Key Distribution

Date: received 15 Jul 2020, last revised 29 Jul 2020

Contact author: emanuele strieder at aisec fraunhofer de, chris frisch@tum de, m pehl@tum de

Available format(s): PDF | BibTeX Citation

Version: 20200729:064021 (All versions of this report)

Short URL: ia.cr/2020/888


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