Bibliographic record
Abstract
Users are encouraged to check in to commercial places in Geo-social networks (GSNs) by offering discounts on purchase. These promotions are commonly known as deals. When a user checks in, GSNs share the check-in record with the merchant. However, these applications, in most cases, do not explain how the merchants handle check-in histories nor do they take liability for any information misuse in this type of services. In practice, a dishonest merchant may share check-in histories with third parties or use them to track users' location. It may cause privacy breaches like robbery, discovery of sensitive information by combining check-in histories with other data, disclosure of visits to sensitive places, etc. In this work, we investigate privacy issues arising from the deal redemptions in GSNs. We propose a privacy framework, called Redeem with Privacy (RwP), to address the risks. RwP works by releasing only the minimum information necessary to carry out the commerce to the merchants. The framework is also equipped with a recommendation engine that helps users to redeem deals in such a way that their next visit will be less predictable to the merchants. Experimental results show that inference attacks will have low accuracy when users check in using the framework's recommendation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".