A Blockchain-based Privacy-Preserving Recommendation Mechanism
Bibliographic record
Abstract
Recommendation system is widely used to predict users' interests and provide targeted products for them, which effectively facilitates users in the era of big data where information overload problem is prevalent. Unfortunately, massive data closely related to users' privacy is in high demand to produce more accurate predictions. In this case, the collection and transmission of such data is communication costly; to process and analyze such data is of high possibility to compromise users' privacy. In this paper, we propose a privacy-preserving recommendation mechanism based on blockchain, which well addresses these problems. Leveraging the inherent advantages of blockchain, we establish a completely distributed model mitigating the risk of privacy disclosure caused by central data storage. Moreover, we combine Inter-Planetary File System with blockchain to greatly improve the communication efficiency. We also introduce local sensitive hashing and local differential privacy into proposed mechanism to reduce the computation load and provide a strong privacy guarantee. The experimental results demonstrate that the proposed mechanism shows better performance on privacy preservation while maintaining desirable recommendation accuracy when compared with the baseline.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".