SRAM Physically Unclonable Functions for Smart Home IoT Telehealth Environments
Bibliographic record
Abstract
This chapter provides an analysis of the characteristics and performance of SRAM physically unclonable functions (PUFs). It discusses the literature related to the use of PUFs for authentication and secure key exchange. The chapter reviews the architecture of a telehealth system where the smart home is the target for the healthcare delivery. It also discusses PUFs and using secure sketch and fuzzy extractors to remove the dynamic noise from the PUF response. The chapter discusses the use of convolutional coding as a means of generating the helper data without revealing the IoT device response when a challenge is issued. The structure of SRAM PUFs is presented and a novel NOR-based SRAM is discussed. A statistical model of the SRAM PUF is also developed. The chapter proposes three algorithms for issuing the PUF challenge-response pair data and their effect on system design. It discusses the attacks targeting smart homes, especially deep learning attacks.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".