Privacy-Preserving Authentication Protocols for IoT Devices Using the SiRF PUF
Bibliographic record
Abstract
Authentication between IoT devices is important for maintaining security, trust and data integrity in an edge device ecosystem. The low-power, reduced computing capacity of the IoT device makes public-private, certificate-based forms of authentication impractical, while other lighter-weight, symmetric cryptography-based approaches, such as message authentication codes, are easy to spoof in unsupervised environments where adversaries have direct physical access to the device. Such environments are better served by security primitives rooted in the hardware with capabilities exceeding those available in cryptography-only frameworks. A key foundational hardware security primitive is the physical unclonable function or PUF. PUFs are well known for removing the need to store secrets in secure non-volatile memories, and for providing very large sets of authentication credentials. In this article, we describe two PUF-based mutual authentication protocols rooted in the entropy provided by a strong PUF. The security properties of the authentication protocols, called COBRA and PARCE, are evaluated in hardware experiments on SoC-based FPGAs, and under extended industrial-standard operating conditions. A codesign-based system architecture is presented in which the SiRF PUF and core authentication functions are implemented in the programmable logic as a secure enclave, while network and database operations are implemented in software on an embedded microprocessor.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".