Implementation of a Partial-Order Data Security Model for the Internet of Things (IoT) Using Software-Defined Networking (SDN)
Bibliographic record
Abstract
Data security on the Internet of Things (IoT) is usually implemented through encryption. This paper presents a solution based on routing, in which data are forwarded only to entities that are intended to receive them according to security requirements of secrecy (also called confidentiality), integrity, and conflicts. Our solution is generic in the sense that it can be used in any network, together with encryption as appropriate. We use the fact that, in any network, security requirements generate a partial order of equivalence classes of entities, and each entity can be labeled according to the position of its equivalence class in the partial order. Routing tables among entities can be compiled using the labels. The method is demonstrated in this paper for software-defined networking (SDN) routers and controllers. We propose a centralized IoT architecture with a cloud structure using SDN as networking infrastructure, where storage entities (i.e., cloud servers) are associated with application entities. A small ‘hospital’ example is shown for illustration. Procedures for network reconfigurations are presented. We also demonstrate the method for the normal case where different partial orders, representing distinct but concurrent security requirements, coexist among a set of entities. The method proposed does not impose an overhead on the normal functioning of SDN networks since it requires calculations only when the network must be reconfigured because of administrative intervention or policies. These occasional updates can be done efficiently and offline.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".