How to Compress (Reusable) Garbled Circuits.
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it