Efficient PUF-Based Key Generation in FPGAs Using Per-Device Configuration
Bibliographic record
Abstract
Reconfigurable systems often require secret keys to encrypt and decrypt data. Applications requiring high security commonly generate keys based on physical unclonable functions (PUFs), circuits that use random manufacturing variations to produce secret keys that are unique to each device. Implementing PUFs on field-programmable gate arrays (FPGAs) is usually difficult, because the designer has limited control over layout, and each PUF system requires a large area overhead to correct errors in the PUF response bits. In this paper, we extend the state of the art for FPGA-based weak PUFs using a novel methodology of per-device configuration and a new PUF variant derived from the popular FPGA-specific Anderson PUF. The PUF is evaluated using Xilinx XC7Z020 programmable systemon-chips from the Virtex-7 family on Zynq ZedBoard platforms. The design we propose has several advantages over existing work including the Anderson PUF on which it is based. Our design is tunable to minimize the response bias and can be implemented using the common SLICEL components on Xilinx FPGAs. Moreover, the proposed PUF design enables an efficient per-device configuration that reduces bit error rate by over 10× at room temperature and improves response stability by over 2× across all temperatures. We demonstrate that the proposed per-device PUF configuration step leads to roughly 2× savings in area resources for PUFs and error correction as used in key generation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".