How to Compress (Reusable) Garbled Circuits.
Bibliographic record
Abstract
A fundamental question about (reusable) circuit garbling schemes is: how small can the garbled circuit be? Our main result is a reusable garbling scheme which produces garbled circuits that are the same size as the original circuit plus an additive poly(λ) bits, where λ is the security parameter. Save the additive poly(λ) factor, this is the best one could hope for. In contrast, all previous constructions of even single-use garbled circuits incurred a multiplicative poly(λ) blowup. Our techniques result in constructions of attribute-based and (single key secure) functional encryption schemes where the secret key of a circuit C consists of C itself, plus poly(λ) additional bits. All of these constructions are based on the subexponential hardness of the learning with errors problem. We also study the dual question of how short the garbled inputs can be, relative to the original input. We demonstrate a (different) reusable circuit garbling scheme, based on multilinear maps, where the size of the garbled input is the same as that of the original input, plus a poly(λ) factor. This improves on the result of Applebaum, Ishai, Kushilevitz and Waters (CRYPTO 2013) who showed such a result for single-use garbling. Similar to the above, this also results in attribute-based and (single key secure) functional encryption schemes where the size of the ciphertext encrypting an input x is the same as that of x, plus poly(λ) additional bits.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".