Supporting Material To "Sifa: Exploiting Ineffective Fault Inductions On Symmetric Cryptography"
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
Supplementary material to the paper "SIFA: Exploiting Ineffective Fault Inductions on Symmetric Cryptography" by Christoph Dobraunig, Maria Eichlseder, Thomas Korak, Stefan Mangard, Florian Mendel, and Robert Primas (CHES 2018, https://eprint.iacr.org/2018/071). Ineffectively faulted AES Ciphertexts for different platforms and with different fault countermeasures in place (including infection-based configurations). Files: */ct_correct.txt: AES ciphertexts where a fault was induced during the encryption, but did not change the ciphertext (decimal, CSV, 1 row per ciphertext) */round_keys.txt: 11 expanded AES round keys used for all ciphertexts (decimal, CSV, 1 row per round key) */sei_hardware.dat: Results of the statistical key-recovery evaluation. Row i lists the SEI of the right key, the SEI of the best wrong key, and the rank of the correct key after using 4*i of the ciphertexts in ct_correct.txt. Target platforms and setups are described in more detail in the paper.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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