Common modulus attacks on small private exponent RSA and some fast variants (in practice)
Bibliographic record
Abstract
Abstract In this work we re-examine two common modulus attacks on RSA. First, we show that Guo's continued fraction attack works much better in practice than previously expected. Given three instances of RSA with a commonmodulus N and private exponents each smaller than N 0.33 , the attack can factor the modulus about 93% of the time in practice. The success rate of the attack can be increased up to almost 100% by including a relatively small exhaustive search. Next, we consider Howgrave-Graham and Seifert's lattice-based attack and show that a second necessary condition for the attack exists that limits the bounds (beyond the original bounds) once n ≥ 7 instances of RSA are used. In particular, by construction, the attack is limited to private exponents at most N 0.5– ε , given sufficiently many instances, instead of the original bound of N 1– ε . In addition, we also consider the effectiveness of the attacks when mounted against multi-prime RSA and Takagi's variant of RSA. For multi-prime RSA, we show three (or more) instances with a common modulus and private exponents smaller than N 1/3– ε is unsafe. For Takagi's scheme, we show that three or more instances with a common modulus N = p t q is unsafe when all the private exponents are smaller than N 2/(3( t +1))– ε . The results, for both variants, is obtained using Guo's method and are almost always successful with the inclusion of a small exhaustive search. When only two instances are available, Howgrave-Graham and Seifert's attack can be successfully mounted on multiprime RSA, with r primes in the modulus, when the private exponents are both smaller than N (3+ r )/7 r – ε .
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".