Learning from the History: Accurately and Efficiently Aggregating Geospatial Data Under Local Differential Privacy
Bibliographic record
Abstract
Aggregating geospatial data plays a crucial role in location-based services. However, collecting such sensitive data raises concerns about location privacy leakage. Local Differential Privacy (LDP), as a de facto privacy paradigm, has been widely employed to ensure individual location privacy. Nonetheless, existing approaches for aggregating geospatial data under LDP either suffer from compromised accuracy or involve complex computations. In this work, we propose a history-aware geospatial data aggregation framework to enhance both accuracy and efficiency while guaranteeing LDP. To this end, we first investigate an efficient aggregation method, namely General Randomized Response (GRR), and find that its variance of aggregation error follows the sum of two zero-mean binomial distributions. This reveals that multiple aggregations can boost the accuracy of GRR. To obtain multiple aggregations without compromising privacy, we adopt a Markov transition model to complement current aggregations from historical ones. However, learning the Markov transition matrix on perturbed data is challenging. Accordingly, we propose a privacy-aware Markov Transition Matrix Estimation (MTME) algorithm. Finally, we introduce a truth discovery-based refinement algorithm to iteratively derive an accurate aggregated result from multiple inaccurate aggregations. We evaluate our proposed method on two real-world trajectory datasets, and thorough experiments demonstrate its superior accuracy and very low time overhead compared to competitors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".