How to Hide Circuits in MPC: An Efficient Framework for Private Function Evaluation.
Bibliographic record
Abstract
We revisit the problem of general-purpose private function evaluation (PFE) wherein a single party P1 holds a circuit C, while each Pi for 1 ≤ i ≤ n holds a private input xi, and the goal is for a subset (or all) of the parties to learn C(x1,..., xn) but nothing else. We put forth a general framework for designing PFE where the task of hiding the circuit and securely evaluating its gates are addressed independently: First, we reduce the task of hiding the circuit topology to oblivious evaluation of a mapping that encodes the topology of the circuit, which we refer to as oblivious extended permutation (OEP) since the mapping is a generalization of the permutation mapping. Second, we design a subprotocol for private evaluation of a single gate (PFE for one gate), which we refer to as private gate evaluation (PGE). Finally, we show how to naturally combine the two components to obtain efficient and secure PFE. We apply our framework to several well-known general-purpose MPC constructions, in each case, obtaining the most efficient PFE construction to date, for the considered setting. Similar to the previous work we only consider semi-honest adversaries in this paper. • In the multiparty case with dishonest majority, we apply our techniques to the seminal GMW protocol [GMW87] and obtain the first general-purpose PFE with linear complexity in the circuit
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".