Anonymizing location-based RFID data
Bibliographic record
Abstract
In this paper, we study the problem of anonymizing high dimensional location-based RFID data for mining or research purposes. We consider the case where RFID cards are used for purchasing in place of magnetic cards. Databases containing such transactions of card holders could be very huge in number of records (equals to number of users) and dimensions (could be equal to the domain of locations where users are allowed to use their cards). This huge database containing user's purchasing history can be mined to find interesting knowledge. At the same time publication of data would cause re-identification attacks by adversaries who have partial knowledge about transactions. Therefore, before publishing transactional data, it should be made k-anonymous. However, traditional k-anonymity methods were designed to k-anonymize low dimensional databases and are not scalable much to produce good results when it comes to k-anonymous large high dimensional databases. In this paper, we provide a solution modeling k-anonymity principle to protect the privacy in publication of high dimensional databases. We propose greedy approach, which scales much better and in most cases finds solution close to the optimal. The proposed algorithm is experimentally evaluated.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".