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Cryptography 2017, 1(3), 23; doi:10.3390/cryptography1030023

FPGA Implementation of a Cryptographically-Secure PUF Based on Learning Parity with Noise

1
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269, USA
2
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
3
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
*
Author to whom correspondence should be addressed.
Received: 14 October 2017 / Revised: 27 November 2017 / Accepted: 6 December 2017 / Published: 9 December 2017
(This article belongs to the Special Issue PUF-Based Authentication)
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Abstract

Herder et al. (IEEE Transactions on Dependable and Secure Computing, 2017) designed a new computational fuzzy extractor and physical unclonable function (PUF) challenge-response protocol based on the Learning Parity with Noise (LPN) problem. The protocol requires no irreversible state updates on the PUFs for security, like burning irreversible fuses, and can correct for significant measurement noise when compared to PUFs using a conventional (information theoretical secure) fuzzy extractor. However, Herder et al. did not implement their protocol. In this paper, we give the first implementation of a challenge response protocol based on computational fuzzy extractors. Our main insight is that “confidence information” does not need to be kept private, if the noise vector is independent of the confidence information, e.g., the bits generated by ring oscillator pairs which are physically placed close to each other. This leads to a construction which is a simplified version of the design of Herder et al. (also building on a ring oscillator PUF). Our simplifications allow for a dramatic reduction in area by making a mild security assumption on ring oscillator physical obfuscated key output bits. View Full-Text
Keywords: physical unclonable function; learning parity with noise; fuzzy extractor physical unclonable function; learning parity with noise; fuzzy extractor
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Jin, C.; Herder, C.; Ren, L.; Nguyen, P.H.; Fuller, B.; Devadas, S.; van Dijk, M. FPGA Implementation of a Cryptographically-Secure PUF Based on Learning Parity with Noise. Cryptography 2017, 1, 23.

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