Energy Efficiency Analysis of Elliptic Curve Based Cryptosystems
Bibliographic record
Abstract
Energy consumption is an important factor for any cryptoscheme, implemented on devices having limited energy resource. In this paper, we analyze the energy consumption during the Diffie-Hellman key exchange implemented on both supersingular and ordinary elliptic curves. The former protocol is Supersingular Isogeny based Diffie-Hellman (SIDH) and the latter is Elliptic Curve based Diffie-Hellman (ECDH) respectively. Implementations are executed on 64 bit Intel Skylake processor. The energy consumption is analysed for the classical bit security levels of 128 and 192. In this paper, a detailed comparison of power and energy consumption by elliptic curve point addition and doubling operations is presented for affine and standard projective coordinates. Projective coordinates based elliptic curve operations are found to be around 50 to 60 times more energy efficient. We then analyze SIDH and ECDH, implemented using projective coordinates. Our results show that, SIDH consumes around 37 to 47 times more energy in comparison to ECDH for the above mentioned bit security levels.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".