Ultra-Lightweight Encryption for STL Files in IoT-based 3D Printing
Bibliographic record
Abstract
In the expanding landscape of open hardware and software, the preservation of privacy is paramount for individuals, products, and systems.This study focuses on the security implications pertaining to stereolithography (STL) files in the 3D printing domain, within the scope of the Internet of Things (IoT).As business models increasingly rely on copyrighted content to fuel free services, the application of lightweight encryption becomes crucial in safeguarding STL files utilized in 3D printing operations.In cognizance of the unique needs of the IoT, such as reduced energy consumption, efficient computation, and superior performance metrics, an adaptation of the pioneering Ultra-Lightweight encryption algorithm, modified PRESENT, is proposed.Modifications are made within the substitution box (s-box) of the PRESENT algorithm, yielding a version that consumes less computational time and power.This modified s-box fulfills several evaluation criteria for assessing security parameters, including bijective property, nonlinearity, and strict avalanche criteria, suggesting a substantial resistance to breaches.The application of this customized PRESENT algorithm to secure STL files in IoT-linked 3D printing demonstrates its efficacy in protecting sensitive data, even under the restrictive resources of IoT environments.The findings of this study contribute to the ongoing dialogue on the intersection of security and accessibility in the age of open-source hardware and software.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".