References to check:
(can be useful) book: Entropy analysis of physical unclonable functions
(check ChatGPT answer also)
Secret-Key Capacity Regions for Multiple Enrollments With an SRAM-PUF → 2019
Security of helper data schemes for SRAM-PUF in multiple enrollment scenarios → 2017
Helper-less physically unclonable functions and chip authentication → 2014
Theoretical Limits of Helperless Stabilizers for Physically Unclonable Constants → 2014
journal version of Helper-less physically unclonable functions and chip authentication
A soft decision helper data algorithm for SRAM PUFs → 2009
cited by Helper-less physically unclonable functions and chip authentication for the statistical model
A Spatial Majority Voting Technique to Reduce Error Rate of Physically Unclonable Functions → 2013
Important! Analysis of Naive SMV
Since the PUF results are random and exhibit little bias, a single flip in the bits with the majority value will overturn the result of majority voting.
Memory-based PUFs are vulnerable as well: A non-invasive attack against SRAM PUFs
Hamming weight distribution (theory)
PUF KEY RECOVERING USING BRUTE -FORCE ATTACK
https://www.mdpi.com/2079-9268/7/1/2
Temporal Majority Voting (TMV) Error Probability formula
(nice definition of symbols)
Error probability (upper bound) of the author’s proposed voter (called UP/DOWN Counter), defined as:
The expected number of trials needed by the UP/DOWN counter to reach a decision state [28] can be derived as (relevant!)
An SRAM-based PUF with a capacitive digital preselection for a 1E-9 key error probability \^
Error probability formulas for TMV, BCH
Cherry-Picking Reliable PUF Bits With Differential Sequence Coding
Theory behind typical sets
Error Event 1 (Lack of Reliable PUF Bits) and Error Event 2 (Helper Data Overflow) formulas
This work theoretically analyze the relationship between the block size and the reliability of PUF response blocks. In particular, we analyze the effect of the block length on the distribution of reliable PUF bits within each block using the information theoretical concept of typicality.
https://ieeexplore.ieee.org/document/6965637
post-processing by helper data algorithms (HDAs) is indispensable to meet the stringent key requirements: reproducibility, high-entropy, and control.
(the paper is very rich in theoretical proofs - can be useful)
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
formal definitions and efficient secure techniques for
– turning biometric information into keys usable for any cryptographic application, and
– reliably and securely authenticating biometric data
not very relevant: A Novel Security Key Generation Method for SRAM PUF Based on Fourier Analysis
