TransNet: Training Privacy-Preserving Neural Network over Transformed Layer

Summary: TransNet proposes a privacy-preserving collaborative neural network using a transformed layer to support arbitrarily partitioned data, with a server that pools transformed data. It reduces computation and communication compared with MPC/HE, requires no special security on the training server, and achieves near-baseline accuracy across varying participant counts. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12272
Venue: VLDB
Year: 2020
Pagerank: 5.2209769e-05
Overall Rank: 9,786 | 32.86%
DOI: 10.14778/3407790.3407794

Incoming Non-self Citations Over Time

Authors

1. Qijian He (University of Science and Technology Beijing)
2. Wei Yang (University of Science and Technology Beijing)
3. Bingren Chen (University of Science and Technology Beijing)
4. Yangyang Geng (University of Science and Technology Beijing)
5. Liusheng Huang (University of Science and Technology Beijing)

BibTeX Citation

@article{he_vldb20,
        title = {{TransNet: Training Privacy-Preserving Neural Network over Transformed Layer}},
        author = {He, Qijian and Yang, Wei and Chen, Bingren and Geng, Yangyang and Huang, Liusheng},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {1849--1862},
        doi = {10.14778/3407790.3407794},
        url = {https://doi.org/10.14778/3407790.3407794},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,391	Privacy-preserving and Verifiable Causal Prescriptive Analytics	2026	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
70	Privacy-Preserving Data Mining	2000	SIGMOD	0.0003804755
2,319	Secure kNN Computation on Encrypted Databases	2009	SIGMOD	8.7577497e-05
5,248	Functional Mechanism: Regression Analysis under Differential Privacy	2012	VLDB	6.3003629e-05

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