How to Efficiently Evaluate RAM Programs with Malicious Security.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Secure 2-party computation (2PC) is becoming practical for some applications. However, most ap-proaches are limited by the fact that the desired functionality must be represented as a boolean circuit. In response, random-access machines (RAM programs) have recently been investigated as a promising alternative representation. In this work, we present the first practical protocols for evaluating RAM programs with security against malicious adversaries. A useful efficiency measure is to divide the cost of malicious-secure evalu-ation of f by the cost of semi-honest-secure evaluation of f. Our RAM protocols achieve ratios matching the state of the art for circuit-based 2PC. For statistical security 2−s, our protocol without preprocessing achieves a ratio of s; our online-offline protocol has a pre-processing phase and achieves online ratio ∼ 2s / log T, where T is the total execution time of the RAM program. To summarize, our solutions show that the “extra overhead ” of obtaining malicious security for RAM programs (beyond what is needed for circuits) is minimal and does not grow with the running time of the program. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.008 |
| 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 it