Review of Physically Unclonable Functions (PUFs): Structures, Models, and Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Physically unclonable functions (PUFs) are now an essential component for strengthening the security of Internet of Things (IoT) edge devices. These devices are an important component in many infrastructure systems such as telehealth, commerce, industry, etc. Traditionally these devices are the weakest link in the security of the system since they have limited storage, processing, and energy resources. Furthermore they are located in unsecured environments and could easily be the target of tampering and various types of attacks. We review in this work the structure of most salient types of PUF systems such as static RAM static random access memory (SRAM), ring oscillator (RO), arbiter PUFs, coating PUFs and dynamic RAM dynamic random access memory (DRAM). We discuss statistical models for the five most common types of PUFs and identify the main parameters defining their performance. We review some of the most recent algorithms that can be used to provide stable authentication and secret key generation without having to use helper data or secure sketch algorithms. Finally we provide results showing the performance of these devices and how they depend on the authentication algorithm used and the main system parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it