Amortizing Garbled Circuits
Bibliographic record
Abstract
Abstract. We consider secure two-party computation in a multiple-execution setting, where two parties wish to securely evaluate the same circuit multiple times. We design efficient garbled-circuit-based two-party protocols secure against malicious adversaries. Recent works by Lindell (Crypto 2013) and Huang-Katz-Evans (Crypto 2013) have obtained op-timal complexity for cut-and-choose performed over garbled circuits in the single execution setting. We show that it is possible to obtain much lower amortized overhead for cut-and-choose in the multiple-execution setting. Our efficiency improvements result from a novel way to combine a recent technique of Lindell (Crypto 2013) with LEGO-based cut-and-choose techniques (TCC 2009, Eurocrypt 2013). In concrete terms, for 40-bit statistical security we obtain a 2 × improvement (per execution) in com-munication and computation for as few as 7 executions, and require only 8 garbled circuits (i.e., a 5 × improvement) per execution for as low as 3500 executions. Our results suggest the exciting possibility that secure two-party computation in the malicious setting can be less than an order of magnitude more expensive than in the semi-honest setting. 1
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".