SupAUTH: A new approach to supply chain authentication for the IoT
Bibliographic record
Abstract
Abstract Recent advances of the Internet of Things (IoT) technologies have enhanced the use of radio‐frequency identification‐based tracking system to be widely deployed in supply chain management covering every step involved in the flow of merchandise from the supplier to the customer to ensure a trustworthy delivery environment. Such authentication system (also known as path authentication) not only guarantees the merchandise to be available in the right destination with no discrepancies and errors but also ensures the route of the merchandise progress to be valid. This paper outlines the current state‐of‐the‐art cryptographic solutions for path authentication, highlights their properties and weakness, and proposes a novel, privacy‐preserving, and efficient solution. Compared with the existing elliptic curve ElGamal re‐encryption–based solution, our homomorphic message authentication code on arithmetic circuit–based solution offers less memory storage (with limited scalability) and no computational requirement on the reader. Moreover, we allow computational ability inside the tag that articulates a new privacy direction to the state‐of‐the‐art path privacy. This privacy notion helps support the confidentiality of the tag movement in the context of IoT‐enabled cross‐organizational tracking environment where the stakeholders can be from different organizations associated together with the merchandise being delivered. As a potential extension to the path authentication protocol, we further propose a polynomial‐based mutual authentication as a security extension and batch initialization as an efficiency extension. Besides our brief security and privacy analysis, our evaluation shows that the proposed solution can significantly reduce memory requirements on tags with marginal computational overhead to ensure transmission path confidentiality. We observe that SupAUTH requires maximum 513‐bit tag memory and 57.3 ms of processing time during evaluation, which is not only practical but also suitable for any suitable low‐cost radio‐frequency identification deployment in IoT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".