Pool: A Practical OT-based OPRF from Learning with Rounding
Bibliographic record
Abstract
We propose Pool: a conceptually simple post-quantum (PQ) oblivious pseudorandom function (OPRF) protocol, that is round-optimal (with input-independent preprocessing), practically efficient, and has security based on the well-understood hardness of the learning with rounding (LWR) problem. Specifically, our design permits oblivious computation of the LWR-based pseudorandom function Fsk(x) = ⌉ H(x)⊤ ⋅ sk ⌋q,p, for random oracle H: {0,1} * → ℤ qn and uniformly chosen sk∈ {0,1} n. For 128-bits of semi-honest security, the Pool OPRF has an online communication cost of 11.9 kB, and a computational runtime of less than 3 ms on a single thread (via an open-source software implementation). This is more efficient (in either online communication cost or runtime) than constructions from well-known PQ PRFs, and is competitive even with constructions that only conjecture PQ security on lesser-known assumptions. As a result, our design gives high-performance, post-quantum variants of established OPRF applications in multi-party computation and private set operation protocols.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".