Bibliographic record
Abstract
Abstract: Pseudo-random functions (PRFs) introduced by Goldwasser, Goldreich, and Micali (FOCS 1984), are one of the most important building blocks in cryptography. A PRF family is a family of seeded functions {fs}, with the property that no efficient adversary can tell the difference between getting oracle access to a random PRF function fs, and getting oracle access to a truly random function. In this work, we consider the problem of constructing pseudo-random functions that are resilient to leakage. Unfortunately, even if a single bit about the secret seed s ∈ {0, 1} k is leaked, then there is no hope to construct a PRF, since the leakage can simply be the first bit of fs(0), and thus fs(0) is distinguishable from uniform. Therefore, when dealing with leakage, we must relax the definition. We consider the following relaxation: Instead of requiring that for each input x, the value fs(x) looks random, we require that it looks like it has high min-entropy, even given oracle access to fs everywhere except point x. We call such a function family a pseudo-entropy function (PEF) family. In particular, a leakage-resilient PEF family has the property that given leakage L(s) and given oracle access to fs, it is hard to predict fs on any input that was not queried. We construct such a leakage-resilient PEF family under the DDH assumption (or more generally, assuming the existence of lossy functions with the property that the output size is not much larger than the input size). We also show that leakage-resilient PEFs imply leakage-resilient random-input PRFs, where the requirement is that for a random input r, the value fs(r) looks uniform, even given the leakage L(s) and given oracle access to fs anywhere accept at point r (the leakage L(s) is independent of r, but the oracle fs is present even after the pair (r, fs(r)) is given).
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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 | 0.015 |
| 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 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".