Laconic Evaluation of Branching Programs from the Diffie-Hellman Assumption
Bibliographic record
Abstract
Secure two-party computation (2PC) enables two parties to compute a function f on their joint inputs while keeping their inputs private. Laconic cryptography is a special type of 2PC in which this is done with asymptotically optimal communication in only two rounds of communication. The party who sends the first message is called the receiver and the party who replies with the second message is called the sender. \nLaconic cryptography considers the case of asymmetric input sizes, where the receiver's input is much larger than the sender's input or vice versa. As such, the size of the messages sent cannot depend on the size of the larger input. For example, if x_R is the receiver's input, x_S is the sender's input, and |x_R| >> |x_S|, then the protocol's communication cost cannot depend on |x_R|, but it may depend on |x_S|. \n \nPrevious works have shown protocols can be built for laconic oblivious transfer (OT) [Cho et al. CRYPTO 2017] and laconic private set intersection (PSI) [Alamati et al. TCC 2021] from the Diffie-Hellman assumption. Quach, Wee, and Wichs [FOCS 2018] give a construction for laconic 2PC for general functionalities based on the Learning with Errors (LWE) assumption. \nIn this work, we bridge the gap by giving a laconic protocol for the evaluation of branching programs (BPs) from the Diffie-Hellman assumption. In this setting, the receiver holds a large branching program BP and the sender holds a short input x. Our protocol allows the receiver to learn x if and only if BP(x) =1, and nothing more. The communication cost only grows with the size of x and the depth of BP, and does not further depend on the size of BP. Our construction can be used to realize PSI and private set union (PSU) functionalities and can handle unbalanced BPs and BPs with wildcards.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".