Strengthening PUFs using Composition
Bibliographic record
Abstract
We explore the idea of composing PUFs with the intent that the resultant PUF is stronger than the constituent PUFs. Prior work has proposed a construction, which subsequent work has shown to be weak. We revisit this prior construction and observe that it is actually weaker than previously thought when the constituent PUFs are arbiter PUFs. This weakness is demonstrated via our adaptation of the previously proposed Logistic Regression (LR) attack. We then propose new constructions called PUFs-composed-with-PUFs (PoP). In particular, we retain a two-layer construction, but allow the same input to the composite PUF to be input to more than one constituent PUF at the first layer. We explore this family of constructions, with arbiter PUFs serving as the constituent PUFs. In particular, we identify several axes which we can vary, and empirically study the resilience of our constructions compared to the prior construction and one another from the standpoint of LR attacks. As insight into why our family of constructions is stronger, we prove, under some idealized conditions, that the lower-bound on an attacker is indeed higher under our constructions than the upper-bound on an attacker for the prior construction. As such, our work suggests that composition can be a promising approach to strengthening PUFs, contrary to what prior work suggests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".