COKV: Key-Value Data Collection With Condensed Local Differential Privacy
Bibliographic record
Abstract
Local differential privacy (LDP) provides lightweight and provable privacy protection and has wide applications in private data collection. Key-value data, as a popular NoSQL structure, requires simultaneous frequency and mean estimations of each key, which poses a challenge to traditional LDP-based collection methods. Despite many schemes proposed for the privacy protection of key-value data, they inadequately solve the condensed perturbation for keys and the advanced combination of privacy budgets, leading to suboptimal estimation accuracy. To address this issue, we propose an efficient key-value collection scheme (COKV) with tight privacy budget composition. In our scheme, we first design a padding and sampling protocol for key-value data to avoid privacy budget splitting. Second, to enhance the utility of key perturbation, we design a key perturbation primitive and optimize the perturbation range to improve computational efficiency. After that, we propose a key-value association perturbation algorithm whose value perturbation strategy guarantees the output expectation equals the original value. Finally, we demonstrate that through a tight privacy budget composition, COKV can provide higher data utility under the same privacy level. Theoretical analysis shows that COKV possesses lower frequency and mean estimations variance. Extensive experiments on both synthetic and real-world datasets also indicate that COKV outperforms the current state-of-the-art methods for secure key-value data collection.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".