Oblivious decision program evaluation
Bibliographic record
Abstract
In this study, the authors design efficient protocols for a number of ‘oblivious decision program (DP) evaluation’ problems. Consider a general form of the problem where a client who holds a private input interacts with a server who holds a private DP (e.g. a decision tree or a branching program) with the goal of evaluating his input on the DP without learning any additional information. Many known private database query problems such as symmetric private information retrieval and private keyword search can be formulated as special cases of this problem. Most of the existing works on the same problem focus on optimising communication. However, in some environments (supported by a few experimental studies), it is the computation and not the communication that may be the performance bottleneck. In this study, we design ‘computationally efficient’ protocols for the above general problem, and a few of its special cases. In addition to being one‐round and requiring a small amount of work by the client (in the RAM model), the proposed protocols only require a small number of exponentiations (independent of the server's input) by both parties. The proposed constructions are, in essence, efficient and black‐box reductions of the above problem to 1‐out‐of‐2 oblivious transfer. It is proved that the proposed protocols secure (private) against ‘malicious’ adversaries in the standard ideal/real‐world simulation‐based paradigm.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".