FleXOR: Flexible garbling for XOR gates that beats free-XOR.
Bibliographic record
Abstract
Most implementations of Yao’s garbled circuit approach for 2-party secure computation use the free-XOR optimization of Kolesnikov & Schneider (ICALP 2008). We introduce an alternative technique called flexible-XOR (fleXOR) that generalizes free-XOR and offers several advantages. First, fleXOR can be instantiated under a weaker hardness assumption on the underlying cipher/hash function (related-key security only, compared to related-key and circular security required for free-XOR) while maintaining most of the performance improvements that free-XOR offers. Alternatively, even though XOR gates are not always “free ” in our approach, we show that the other (non-XOR) gates can be optimized more heavily than what is possible when using free-XOR. For many circuits of cryptographic interest, this can yield a significantly (over 30%) smaller garbled circuit than any other known techniques (including free-XOR) or their combinations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".