Efficient and Privacy-Preserving Subgraph Matching Queries in Graph Federation
Why this work is in the frame
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Bibliographic record
Abstract
Graph technology has been attracting interest due to its ability in modeling complex network and real-world relationships in various applications. Subgraph matching queries are useful tools that can be used to extract structural insights from graph dataset. As the accuracy of subgraph matching queries increases with graph size, it is natural to consider providing subgraph matching query services over a graph federation, which can form a larger graph by combining graphs from multiple data owners. However, the downside combining data is that it may provoke privacy concerns related to the graph datasets and user queries. Although many schemes have been proposed for privacy-preserving subgraph matching queries, they either cannot be extended to graph federation scenarios or do not consider query privacy. Aiming at this challenge, in this paper we construct an efficient and privacy-preserving subgraph matching query scheme in graph federation with two data owners. In the proposed scheme, the two data owners jointly compute the neighboring signatures of all vertices without disclosing their graph datasets to each other. Upon receiving a subgraph matching query, the data owners together respond with a subgraph which includes all subgraphs matching the pattern in the combined graph. Security analysis shows that our proposed scheme can well preserve data and query privacy. Extensive experiments further demonstrate that the scheme is efficient in terms of computation and communication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it