An Efficient Bloom Filter-based Range Query Scheme Under Local Differential Privacy
Bibliographic record
Abstract
While crowdsourcing for data collection has become increasingly popular in data-driven applications, privacy remains a significant challenge. This paper presents an effective scheme for conducting range queries under local differential privacy (LDP) in crowdsourcing applications, which addresses the privacy challenges that arise in such scenarios. In particular, our proposed scheme utilizes Prefix Encoding (PE) and Bloom Filter (BF) techniques to convert a large domain into a binary domain for improved query accuracy. When responding to the query, individual users can check a Bloom filter to determine whether their private item is within the query range and use the Basic Randomized Response (BRR) technique to perturb their result for achieving ε-LDP. Detailed security analysis shows that our proposed scheme can preserve user’s item privacy and also keep an external passive attacker from learning the query range. In addition, performance evaluation shows that the proposed scheme is efficient in terms of computational cost and communication overhead, while effectively balancing range query accuracy and privacy.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".