Sourcing Trust From Peers with Physical Unclonable Functions
Bibliographic record
Abstract
Traditional authentication schemes rely heavily on trusted third parties that act as certificate authorities to source authentication credentials. In this paper, we describe an offline, end-to-end mutual authentication and session key generation protocol, called PUF-based Peer Trust (PPT), where authentication and session key generation between two parties (Alice and Bob) is implemented using physical unclonable functions (PUFs) and authentication information from neighboring peer devices (Charlies). Trust among the parties is established by defining an effort-reward model based on Contract Theory that leverages the trust beliefs provided by a set of peer devices. Alice autonomously selects a set of trusted Teds from the set of untrusted Charlies using a reinforcement learning algorithm based on Stochastic Learning Automata. These Teds participate in a peer trust authentication process designed to help Alice decide if Bob is trustworthy. The Teds are incentivized to provide accurate information (effort) via a reward system based on trust scores. Alice computes a personalized trust belief distribution for all the Charlies and utilizes it to eventually update the trust scores of the selected Teds based on their contribution to Bob's authentication process. An experimental evaluation of the model is carried out using a set of PUF-instantiated FPGAs. Detailed numerical results are presented demonstrating the effectiveness of the proposed scheme and its ability to correctly decide if Bob is trustworthy based on incomplete information provided by peers.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".