A community search method for graph federations

By combining obfuscation encryption and R-Tree indexing techniques with federated graph indexing and connected component equivalence relation models, the problem of balancing privacy protection and query efficiency in graph federation computing is solved, achieving efficient and accurate community search.

CN122204550BActive Publication Date: 2026-07-24ZHEJIANG UNIV
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Patent Information

Application Number
CN202610652065.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-13
Publication Date
2026-07-24
Estimated Expiration
2046-05-13

AI Technical Summary

Technical Problem

Existing technologies in graph federation computing suffer from the problem of balancing privacy protection and query efficiency. Especially when dealing with large-scale graph processing and complex queries, traditional frameworks cannot simultaneously meet the needs of multi-party participation, data security, and query efficiency. Moreover, existing algorithms often only produce high-precision rather than completely accurate community results.

Method used

Obfuscation encryption technology is used to process graph data, a federated graph index is constructed, and fast retrieval is achieved through equivalent plaintext computation. By utilizing a three-party collaborative encryption mechanism and R-Tree index, combined with a connected component equivalence relation model, data privacy is protected and query efficiency is improved.

Benefits of technology

Under the premise of strictly protecting data privacy, the query efficiency and scalability of the federal community search have been greatly improved, achieving efficient and accurate community identification.

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Abstract

The application discloses a community search method for a graph federation, constructs a federated graph index through confusion encryption security, realizes equivalent plaintext calculation on ciphertext, completes index construction offline and completes fast retrieval online, and greatly improves the query efficiency and scalability of the federated community search under the premise of strictly guaranteeing the local graph structure privacy of each data party. Based on the uniqueness and inclusiveness of the federated k-core, the application efficiently calculates according to a predetermined tree structure, greatly improves the efficiency; by combining the domination vector and the minimum bounding rectangle, the R-Tree index about the k-vector is constructed, the efficient search is realized, and the storage cost is reduced; by traversing the equivalent relationship model between the connected components and the R-Tree index and aggregating the k-shell, the final community is obtained by decrypting and restoring the vertex set, and a reliable implementation scheme is provided for the efficient and safe community search task in the graph federation privacy data collaboration scene.
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