PRAC: Round-Efficient 3-Party MPC for Dynamic Data Structures
Bibliographic record
Abstract
We present Private Random Access Computations (PRAC), a 3-party Secure Multi-Party Computation (MPC) framework to support random-access data structure algorithms for MPC with efficient communication in terms of rounds and bandwidth. PRAC extends the state-of-the-art DORAM Duoram with a new implementation, more flexibility in how the DORAM memory is shared, and support for Incremental and Wide DPFs. We then use these DPF extensions to achieve algorithmic improvements in three novel oblivious data structure protocols for MPC. PRAC exploits the observation that a secure protocol for an algorithm can gain efficiency if the protocol explicitly reveals information leaked by the algorithm inherently. We first present an optimized binary search protocol that reduces the bandwidth from O(lg² n) to O(lg n) for obliviously searching over n items. We then present an oblivious heap protocol with rounds reduced from O(lg n) to O(lg lg n) for insertions, and bandwidth reduced from O(lg² n) to O(lg n) for extractions. Finally, we also present the first oblivious AVL tree protocol for MPC where no party learns the data or the structure of the AVL tree, and can support arbitrary insertions and deletions with O(lg n) rounds and bandwidth. We experimentally evaluate our protocols with realistic network settings for a wide range of memory sizes to demonstrate their efficiency. For instance, we observe our binary search protocol provides >27× and >3× improvements in wall-clock time and bandwidth respectively over other approaches for a memory with 2^26 items; for the same setting our heap's extract-min protocol achieves >31× speedup in wall-clock time and >13× reduction in bandwidth.
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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 | 0.003 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".