Bibliographic record
Abstract
To benefit from a location-based service, a person must reveal her location to the service. However, knowing the person's location might allow the service to re-identify the person. Location privacy based on k-anonymity addresses this threat by cloaking the person's location such that there are at least k - 1 other people within the cloaked area and by revealing only the cloaked area to a location-based service. Previous research has explored two ways of cloaking: First, have a central server that knows everybody's location determine the cloaked area. However, this server needs to be trusted by all users and is a single point of failure. Second, have users jointly determine the cloaked area. However, this approach requires that all users trust each other, which will likely not hold in practice. We propose a distributed approach that does not have these drawbacks. Our approach assumes that there are multiple servers, each deployed by a different organization. A user's location is known to only one of the servers (e.g., to her cellphone provider), so there is no single entity that knows everybody's location. With the help of cryptography, the servers and a user jointly determine whether the k-anonymity property holds for the user's area, without the servers learning any additional information, not even whether the property holds. A user learns whether the k-anonymity property is satisfied and no other information. The evaluation of our sample implementation shows that our distributed k-anonymity protocol is sufficiently fast to be practical. Moreover, our protocol integrates well with existing infrastructures for location-based services, as opposed to the previous research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".