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Found 2 papers, 1 papers with code
S-GBDT: Frugal Differentially Private Gradient Boosting Decision Trees
For the Abalone dataset for $\varepsilon=0. 54$ we achieve $R^2$-score of $0. 47$ which is very close to the $R^2$-score of $0. 54$ for the nonprivate version of GBDT.
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers
We provide experimental evidence that blind averaging for SVMs and single Softmax-layer (Softmax-SLP) can have a strong utility-privacy tradeoff: we reach an accuracy of 86% on CIFAR-10 for $\varepsilon$ = 0. 4 and 1, 000 users, of 44% on CIFAR-100 for $\varepsilon$ = 1. 2 and 100 users, and of 39% on federated EMNIST for $\varepsilon$ = 0. 4 and 3, 400 users, all after a SimCLR-based pretraining.
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