Selective MPC: Distributed Computation of Differentially Private\n Key-Value Statistics
Bibliographic record
Abstract
Key-value data is a naturally occurring data type that has not been\nthoroughly investigated in the local trust model. Existing local differentially\nprivate (LDP) solutions for computing statistics over key-value data suffer\nfrom the inherent accuracy limitations of each user adding their own noise.\nMulti-party computation (MPC) maintains better accuracy than LDP and similarly\ndoes not require a trusted central party. However, naively applying MPC to\nkey-value data results in prohibitively expensive computation costs. In this\nwork, we present selective multi-party computation, a novel approach to\ndistributed computation that leverages DP leakage to efficiently and accurately\ncompute statistics over key-value data. By providing each party with a view of\na random subset of the data, we can capture subtractive noise. We prove that\nour protocol satisfies pure DP and is provably secure in the combined DP/MPC\nmodel. Our empirical evaluation demonstrates that we can compute statistics\nover 10,000 keys in 20 seconds and can scale up to 30 servers while obtaining\nresults for a single key in under a second.\n
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".