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Learnability of Optical Physical Unclonable Functions Through the Lens of Learning With Errors

Journal Article · · IEEE Transactions on Information Forensics and Security
 [1];  [2];  [2]
  1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Boston Univ., MA (United States)
  2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
We show that a class of optical physical unclonable functions (PUFs) can be efficiently PAC-learned to arbitrary precision with arbitrarily high probability, even in the presence of intentionally injected noise, given access to polynomially many challenge-response pairs, under mild and practical assumptions about the distributions of the noise and challenge vectors. We motivate our analysis by identifying similarities between the integrated version of Pappu’s original optical PUF design and the post-quantum Learning with Errors (LWE) cryptosystem. We derive polynomial bounds for the required number of samples and the computational complexity of a linear regression algorithm, based on size parameters of the PUF, the distributions of the challenge and noise vectors, and the desired accuracy and probability of success of the regression algorithm. We use a similar analysis to that done by Bootle et al. [“LWE without modular reduction and improved side-channel attacks against BLISS,” in Advances in Cryptology – ASIACRYPT 2018], who demonstrated a learning attack on poorly implemented versions of LWE cryptosystems. This extends the results of Rührmair et al. [“Optical PUFs reloaded,” Cryptology ePrint Archive, 2013], who presented a theoretical framework showing that a subset of this class of PUFs is learnable in polynomial time in the absence of injected noise, under the assumption that the optics of the PUF were either linear or had negligible nonlinear effects. (Rührmair et al. also included an experimental validation of this technique, which of course included measurement uncertainty, demonstrating robustness to the presence of natural noise.) We recommend that the design of strong PUFs should be treated as a cryptographic engineering problem in physics, as PUF designs would benefit greatly from basing their physics and security on standard cryptographic assumptions. Finally, we identify future research directions, including suggestions for how to modify an LWE-based optical PUF design to better defend against cryptanalytic attacks.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
89233218CNA000001
OSTI ID:
2481609
Alternate ID(s):
OSTI ID: 2511270
Report Number(s):
LA-UR--24-22627
Journal Information:
IEEE Transactions on Information Forensics and Security, Journal Name: IEEE Transactions on Information Forensics and Security Vol. 20; ISSN 1556-6013
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

References (34)

FPGA Intrinsic PUFs and Their Use for IP Protection conference January 2007
Strong Machine Learning Attack Against PUFs with No Mathematical Model book January 2016
User-Friendly Tail Bounds for Sums of Random Matrices journal August 2011
PAC learning of arbiter PUFs journal February 2016
Having no mathematical model may not secure PUFs journal March 2017
A PUF taxonomy journal March 2019
Instabilities of Waves in Nonlinear Disordered Media journal July 2000
Stochastic Problems in Physics and Astronomy journal January 1943
Laser Fault Attack on Physically Unclonable Functions conference September 2015
Planting Undetectable Backdoors in Machine Learning Models : [Extended Abstract] conference October 2022
Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures conference October 2022
Lattice PUF: A Strong Physical Unclonable Function Provably Secure against Machine Learning Attacks conference December 2020
Cloning Physically Unclonable Functions conference June 2013
Power-Up SRAM State as an Identifying Fingerprint and Source of True Random Numbers journal September 2009
A Provably Secure Strong PUF Based on LWE: Construction and Implementation journal February 2023
Trapdoor computational fuzzy extractors and stateless cryptographically-secure physical unclonable functions journal January 2017
A PUF Based on a Transient Effect Ring Oscillator and Insensitive to Locking Phenomenon journal March 2014
Extracting secret keys from integrated circuits journal October 2005
A technique to build a secret key in integrated circuits for identification and authentication applications conference January 2004
Physical One-Way Functions journal September 2002
Materials for Optical Information Processing journal November 1984
Nonlinear Optical Materials journal July 1991
On lattices, learning with errors, random linear codes, and cryptography conference May 2005
Modeling attacks on physical unclonable functions conference January 2010
A theory of the learnable journal November 1984
On Ideal Lattices and Learning with Errors over Rings journal November 2013
Lattice Basis Reduction Attack against Physically Unclonable Functions conference October 2015
Continuous LWE conference June 2021
Silicon physical random functions conference January 2002
Silicon photonic physical unclonable function journal May 2017
Secure communications using nonlinear silicon photonic keys journal February 2018
Non-linear optical scattering PUF: enhancing security against modeling attacks for authentication systems journal November 2023
Effects of Kerr Nonlinearity in Physical Unclonable Functions journal November 2022
FPGA Implementation of a Cryptographically-Secure PUF Based on Learning Parity with Noise journal December 2017