ELLIPTIC CURVE CRYPTOGRAPHY: IMPLEMENTATION USING GOOGLE APPS SCRIPT (GAS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) algorithms are two wellknown public-key encryption methods for transmitting data securely over an untrusted network.ECC is increasingly being adopted as an alternative to RSA to provide adequate security to systems and networks with less computational resources such as limited bandwidth, storage, and power.Because of the complexity of mathematics involve, it is a challenge to students and practitioners alike without adequate mathematical foundations to apply the ECC algorithm to compute appropriate public keys and encrypt and decrypt messages.In this paper the authors attempt to show how the ECC algorithm can be modeled with Google Sheets, and how Google App Scripts (GAS) can easily and effectively be used to encrypt and decrypt messages.The authors find that the ECC algorithm can be easily demonstrated to learners utilizing Google Sheets as the foundation.The authors also discuss the limitations and practical issues encountered when using Google Sheets and GAS.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it