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: 12084
Venue: VLDB
Year: 2020
Pagerank: 4.3109001e-05
Overall Rank: 9,654 | 32.84%
DOI: 10.14778/3407790.3407794

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,101	Privacy-preserving and Verifiable Causal Prescriptive Analytics	2026	SIGMOD	4.1945683e-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
40	Privacy-Preserving Data Mining	2000	SIGMOD	0.00074232718
2,655	Secure kNN Computation on Encrypted Databases	2009	SIGMOD	8.3622816e-05
4,600	Functional Mechanism: Regression Analysis under Differential Privacy	2012	VLDB	6.0578625e-05

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