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Scalable Privacy-Preserving Shortest Path Distance Computation via 2-Hop Labeling in MPC

Summary: PrivHop: MPC-based shortest-path distance with 2-hop labeling, turning global queries into boundary-graph queries via an offline index, then using privacy-aware dynamic pruning to cut iterations. Key result: privacy-preserving SSSP/SD computation scales to million-node graphs with up to 10^6× runtime/communication gains. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID
7712
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,498 | 27.98%
DOI
10.1145/3786695

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BibTeX Citation

@inproceedings{wang_sigmod26,
        title = {{Scalable Privacy-Preserving Shortest Path Distance Computation via 2-Hop Labeling in MPC}},
        author = {Wang, Huizhong and Zeng, Yuanyuan and Chen, Kun and Dong, Wei and Ma, Chenhao},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786695},
        url = {https://dl.acm.org/doi/10.1145/3786695},
        year = {2026}
}

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