Implementation of a leakage-resilient ElGamal key encapsulation mechanism
Bibliographic record
Abstract
Leakage-resilient cryptography aims to extend the rigorous guarantees achieved through the provable security paradigm to physical implementations. The constructions designed on basis of this new approach inevitably suffer from an Achilles heel: a bounded leakage assumption is needed. Currently, a huge gap exists between the theory of such designs and their implementation to confirm the leakage resilience in practice. The present work tries to narrow this gap for the leakage-resilient bilinear ElGamal key encapsulation mechanism (BEG-KEM) proposed by Kiltz and Pietrzak in 2010. Our first contribution is a variant of the bounded leakage and the only-computation-leaks model that is closer to practice. We weaken the restriction on the image size of the leakage functions in these models and only insist that the inputs to the leakage functions have sufficient min-entropy left, in spite of the leakage, with no limitation on the quantity of this leakage. We provide a novel security reduction for BEG-KEM in this relaxed leakage model using the generic bilinear group axiom. Secondly, we show that a naive implementation of the exponentiation in BEG-KEM makes it impossible to meet the leakage bound. Instead of trying to find an exponentiation algorithm that meets the leakage axiom (which is a non-trivial problem in practice), we propose an advanced scheme, BEG-KEM+, that avoids exponentiation by a secret value, but rather uses an encoding into the base group due to Fouque and Tibouchi. Thirdly, we present a software implementation of BEG-KEM+ based on the Miracl library and provide detailed experimental results. We also assess its (theoretical) resistance against power analysis attacks from a practical perspective, taking into account the state-of-the-art in side-channel cryptanalysis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".