Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics

Summary: Bifrost delivers redundancy-free secret-shared joins using only ECDH-PSI and two-party oblivious shuffles, eliminating iPrivJoin’s OPPRF and reducing shuffle rounds via dual mapping. At 100 GB, it achieves 2.54–22.32× speedup and 84–89% lower communication, improving downstream SDA. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: he6d4425485ca0bf1
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,704 | 28.04%
DOI: 10.14778/3797919.3797925

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Shuyu Chen (Fudan University)
2. Mingxun Zhou (Hong Kong University of Science and Technology)
3. Haoyu Niu (Fudan University)
4. Guopeng Lin (Fudan University)
5. Weili Han (Fudan University)

BibTeX Citation

@article{chen_vldb26,
        title = {{Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics}},
        author = {Chen, Shuyu and Zhou, Mingxun and Niu, Haoyu and Lin, Guopeng and Han, Weili},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {6},
        pages = {1156--1169},
        doi = {10.14778/3797919.3797925},
        url = {https://doi.org/10.14778/3797919.3797925},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
68	Privacy-Preserving Data Mining	2000	SIGMOD	0.00037958605
1,277	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00011237888
1,495	SMCQL: Secure Querying for Federated Databases	2017	VLDB	0.00010484897
2,043	A Worst-Case Optimal Multi-Round Algorithm for Parallel Computation of Conjunctive Queries	2017	PODS	9.1406885e-05
2,074	Efficient Oblivious Database Joins	2020	VLDB	9.0841817e-05
3,636	Advanced Join Strategies for Large-Scale Distributed Computation	2014	VLDB	7.1471007e-05
4,883	Hu-Fu: Efficient and Secure Spatial Queries over Data Federation	2022	VLDB	6.3710505e-05

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