Personalized privacy-preserving semi-centralized recommendation system in a social network
Bibliographic record
Abstract
In the contemporary era of big data, recommendation systems play a crucial role in guiding our daily decision-making amidst an overwhelming array of choices. Personalized recommendations have become increasingly popular by tailoring suggestions to user profiles, preferences, and interests. While many existing systems rely on centralizing data for making recommendations, the revelation of sensitive information poses a significant privacy concern. Studies indicate the potential to de-identify anonymous users, exposing details such as political views or sexual orientations through seemingly innocuous data, like movie ratings. In this paper, we introduce a personalized privacy-preserving semi-centralized recommendation system in a social network known as trust-based social network (TSN) to address these privacy challenges. TSN addresses privacy concerns by semi-centralizing data, treating each node in the network as an independent social entities. Data are distributed to social entities within trusted social networks, and the recommendation service provider only collects obfuscated data from social entities through the adoption of a differential-privacy mechanism. Consequently, data within TSN are either protected within local trusted social networks or obfuscated outside of these networks. The final recommendation is generated by combining local suggestions from the trusted social network with obfuscated global suggestions from the service provider. The emphasis on local suggestions ensures highly personalized recommendations. Evaluation results demonstrate that TSN achieves high accuracy in recommendations while effectively safeguarding user privacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".