Garbled Circuits Checking Garbled Circuits: More Efficient and Secure Two-Party Computation.
Bibliographic record
Abstract
Applying cut-and-choose techniques to Yao’s garbled circuit protocol has been a promising approach for designing efficient Two-Party Computation (2PC) with malicious and covert security, as is evident from various optimizations and software implementations in the recent years. We revisit the security and efficiency properties of this popular approach and propose alternative constructions and definitions that are more suitable for use in practice. • We design an efficient fully-secure malicious 2PC protocol for two-output functions that only requires O(t|C|) symmetric-key operations (with small constant factors) where |C | is the circuit size and t is a statistical security parameter. This is essentially the optimal complexity for protocols based on cut-and-choose, resolving a main question left open by the previous work on the subject. Our protocol utilizes novel techniques for enforcing garbler’s input consistency and handling twooutput functions that are more efficient than all prior solutions. • Motivated by the goal of eliminating the all-or-nothing nature of 2PC with covert security (that privacy and correctness are fully compromised if the adversary is not caught in the challenge phase), we propose a new security definition for 2PC that strengthens the guarantees provided by the standard
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".