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XORBoost: Tree Boosting in the Multiparty Computation Setting [article]

Kevin Deforth, Marc Desgroseilliers, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev, Marius Vuille
2021 IACR Cryptology ePrint Archive  
We present a novel protocol XORBoost for both training gradient boosted tree models and for using these models for inference in the multiparty computation (MPC) setting. Similarly to [2], our protocol is the first one supporting training for generically split datasets (vertical and horizontal splitting, or combination of those) while keeping all the information about the features and thresholds associated with the nodes private, thus, having only the depths and the number of the binary trees as
more » ... public parameters of the model. By using optimization techniques reducing the number of oblivious permutation evaluations as well as the quicksort and real number arithmetic algorithms from the recent Manticore MPC framework [5], we obtain a scalable implementation operating under information-theoretic security model in the honest-butcurious setting with a trusted dealer. On a training dataset of 25,000 samples and 300 features in the 2-player setting, we are able to train 10 regression trees of depth 4 in less than 5 minutes per tree (using histograms of 128 bins).
dblp:journals/iacr/DeforthDGGJV21 fatcat:g4xzktlbhngkpnissezbn366ga

Manticore: Efficient Framework for Scalable Secure Multiparty Computation Protocols [article]

Sergiu Carpov, Kevin Deforth, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev, Jonathan Katz, Iraklis Leontiadis, M. Mohammadi, Abson Sae-Tang, Marius Vuille
2021 IACR Cryptology ePrint Archive  
We propose a novel MPC framework, Manticore, in the multiparty setting, with full threshold and semi-honest security model, supporting a combination of real number arithmetic (arithmetic shares), Boolean arithmetic (Boolean shares) and garbled circuits (Yao shares). In contrast to prior work [34, 32] , Manticore never overflows, an important feature for machine learning applications. It achieves this without compromising efficiency or security. Compared to other overflow-free recent techniques
more » ... uch as MP-SPDZ [17] that convert arithmetic to Boolean shares, we introduce a novel highly efficient modular lifting/truncation method that stays in the arithmetic domain. We revisit some of the basic MPC operations such as real-valued polynomial evaluation, division, logarithms, exponentials and comparisons by employing our modular lift in combination with existing efficient conversions between arithmetic, Boolean and Yao shares. Furthermore, we provide a highly efficient and scalable implementation supporting logistic regression models with realworld training data sizes and high numerical precision through PCA and blockwise variants (for memory and runtime optimizations). On a dataset of 50 million rows and 50 columns distributed among two players, it completes in one day with at least 10 decimal digits of precision. Our logistic regression solution placed first at Track 3 of the annual iDASH'2020 Competition. Finally, we mention a novel oblivious sorting algorithm built using Manticore.
dblp:journals/iacr/CarpovDGGJKLMSV21 fatcat:mjx6bpo5obbzbjmxzf3bkaotra

Supplementum 229: swiss orthopaedics, 78th annual meeting

2018 Swiss Medical Weekly  
Anne-Constance Franz¹, Manja Deforth, Lukas Zwicky, Christine Schweizer, Prof. Dr.  ...  Dr Matthias Vautrin, Dr Kevin Moerenhout, Dr Gilles Udin, Prof. Dr.  ... 
doi:10.4414/smw.2018.20431 fatcat:4i5n7e7y3fafpjxutee7qoxsga