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Rock'n'roll PUFs: Crafting Provably Secure PUFs from Less Secure Ones

16 pagesPublished: September 6, 2019

Abstract

The era of PUFs has been characterized by the efforts put into research and the devel- opment of PUFs that are resilient against attacks, in particular, machine learning (ML) attacks. Due to the lack of systematic and provable methods for this purpose, we have witnessed the ever-continuing competition between PUF designers/ manufacturers, crypt- analysts, and of course, adversaries that maliciously break the security of PUFs. This is despite a series of acknowledged principles developed in cryptography and complexity theory, under the umbrella term “hardness amplification”. This paper aims at narrowing the gap between these studies and hardware security, specifically for applications in the domain of PUFs. To this end, this paper provides an example of somewhat hard PUFs and demonstrates how to build a strongly secure construction out of these considerably weaker primitives. Our theoretical findings are discussed in an exhaustive manner and supported by the silicon results captured from real-world PUFs.

Keyphrases: hardness amplification, machine learning, physically unclonable function

In: Karine Heydemann, Ulrich Kühne and Letitia Li (editors). Proceedings of 8th International Workshop on Security Proofs for Embedded Systems, vol 11, pages 33-48.

BibTeX entry
@inproceedings{PROOFS2019:Rocknroll_PUFs_Crafting_Provably,
  author    = {Fatemeh Ganji and Shahin Tajik and Pascal Stauss and Jean-Pierre Seifert and Domenic Forte and Mark Tehranipoor},
  title     = {Rock'n'roll PUFs: Crafting Provably Secure PUFs from Less Secure Ones},
  booktitle = {Proceedings of 8th International Workshop on Security Proofs for Embedded Systems},
  editor    = {Karine Heydemann and Ulrich Kühne and Letitia Li},
  series    = {Kalpa Publications in Computing},
  volume    = {11},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/wnz9},
  doi       = {10.29007/nbm3},
  pages     = {33-48},
  year      = {2019}}
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