Preserving privacy and fairness in peer-to-peer data integration
Bibliographic record
Abstract
Peer-to-peer data integration - a.k.a. Peer Data Management Systems (PDMSs) - promises to extend the classical data integration approach to the Internet scale. Unfortunately, some challenges remain before realizing this promise. One of the biggest challenges is preserving the privacy of the exchanged data while passing through several intermediate peers. Another challenge is protecting the mappings used for data translation. Protecting the privacy without being unfair to any of the peers is yet a third challenge. This paper presents a novel query answering protocol in PDMSs to address these challenges. The protocol employs a technique based on noise selection and insertion to protect the query results, and a commutative encryption-based technique to protect the mappings and ensure fairness among peers. An extensive security analysis of the protocol shows that it is resilient to several possible types of attacks. We implemented the protocol within an established PDMS: the Hyperion system. We conducted an experimental study using real data from the healthcare domain. The results show that our protocol manages to achieve its privacy and fairness goals, while maintaining query processing time at the interactive level.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".