SpecTre: A Tiny Side-Channel Resistant Speck Core for FPGAs.
Bibliographic record
Abstract
Emerging applications such as the Internet of Things require security solutions that are small and low cost, yet feature solid protection against a wide range of sophisticated attacks. Lightweight cryptographic schemes such as the Speck cipher that was recently proposed by the NSA aim to solve some of these challenges. However, before using Speck in any practical application, sound protection against side-channel attacks must be in place. In this work, we propose a bit-serialized implementation of Speck, to achieve minimal area footprint. We further propose a Speck core that is provably secure against first-order side-channel attacks using a threshold implementation technique which depends on secure multiparty computation. The resulting design is a tiny crypto core that provides AES-like security in under 45 slices on a low-cost Xilinx Spartan 3 FPGA. The first-order side-channel resistant version of the same core needs less than 100 slices. The security of the protected core is validated by state-of-the-art side-channel leakage detection tests.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".