Efficient Server-Aided 2PC for Mobile Phones
Bibliographic record
Abstract
Abstract Secure Two-Party Computation (2PC) protocols allow two parties to compute a function of their private inputs without revealing any information besides the output of the computation. There exist low cost general-purpose protocols for semi-honest parties that can be efficiently executed even on smartphones. However, for the case of malicious parties, current 2PC protocols are significantly less efficient, limiting their use to more resourceful devices. In this work we present an efficient 2PC protocol that is secure against malicious parties and is light enough to be used on mobile phones. The protocol is an adaptation of the protocol of Nielsen et al. (Crypto, 2012) to the Server-Aided setting, a natural relaxation of the plain model for secure computation that allows the parties to interact with a server (e.g., a cloud) who is assumed not to collude with any of the parties. Our protocol has two stages: In an offline stage - where no party knows which function is to be computed, nor who else is participating - each party interacts with the server and downloads a file. Later, in the online stage, when two parties decide to execute a 2PC together, they can use the files they have downloaded earlier to execute the computation with cost that is lower than the currently best semi-honest 2PC protocols. We show an implementation of our protocol for Android mobile phones, discuss several optimizations and report on its evaluation for various circuits. For example, the online stage for evaluating a single AES circuit requires only 2.5 seconds and can be further reduced to 1 second (amortized time) with multiple executions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".