Efficient Privacy-Preserving Approaches for Trajectory Datasets
Bibliographic record
Abstract
The use of credit cards or RFID cards for transaction payment has become ubiquitous in recent years. This transactional data is stored in the form of a trajectory, including a sequence of locations at which the customer used the credit card. Such datasets are sensitive since they store customer's locations and purchasing patterns. Releasing this type of dataset without proper anonymization may breach individual's privacy. Moreover, an adversary can attack the dataset with partial trajectory knowledge to reveal sensitive information about a person. We study trajectory data anonymization techniques for this problem, where a data publisher can construct a safe dataset with minimum information loss. A global suppression algorithm was proposed in [1] for trajectory data anonymization. The algorithm is computationally expensive for large trajectory datasets. We propose a tree-based data structure that significantly reduces the computational cost of the global suppression algorithm. In an experiment with a real-world dataset, our data structure performs global suppression in 50% less time than the state- of-the-art algorithm.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".