On the Privacy Implications of Location Semantics
Bibliographic record
Abstract
Abstract Mobile users increasingly make use of location-based online services enabled by localization systems. Not only do they share their locations to obtain contextual services in return (e.g., ‘nearest restaurant’), but they also share, with their friends, information about the venues (e.g., the type, such as a restaurant or a cinema) they visit. This introduces an additional dimension to the threat to location privacy: location semantics, combined with location information, can be used to improve location inference by learning and exploiting patterns at the semantic level (e.g., people go to cinemas after going to restaurants). Conversely, the type of the venue a user visits can be inferred, which also threatens her semantic location privacy. In this paper, we formalize this problem and analyze the effect of venue-type information on location privacy. We introduce inference models that consider location semantics and semantic privacy-protection mechanisms and evaluate them by using datasets of semantic check-ins from Foursquare, totaling more than a thousand users in six large cities. Our experimental results show that there is a significant risk for users’ semantic location privacy and that semantic information improves inference of user locations.
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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 | 0.025 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".