Privacy-preserving social recommendations in geosocial networks
Bibliographic record
Abstract
Geosocial networks like Foursquare have enabled people to conveniently share their whereabouts with their friends online, such as sharing check-ins at visited venues. This information could be utilized by recommender systems to improve the recommendation accuracy, known as social recommendations. However, incorporating social context into recommender systems introduces new privacy threats to users. We design a framework to achieve the benefits of social recommendations while preserving the privacy of social relations and considering the business interests of the service provider (SP). Namely, we propose that each user manages social relations locally and participates in computing social recommendations without revealing social relations to the SP and without the SP revealing proprietary information to a user. In addition, we identify three classes of inference attacks where the SP may infer the existence of social relations by monitoring users' individual check-in histories. Furthermore, we propose using private check-ins to defend against such attacks. Finally, we conduct a comprehensive performance evaluation over large-scale real-world datasets. The results suggest that the proposed privacy-preserving framework is feasible on a smart phone and only slightly affects the overall performance of recommender systems.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".