MesaTEE GBDT-RS : a fast and secure GBDT library, supporting TEEs such as Intel SGX and ARM TrustZone

MesaTEE GBDT-RS is a gradient boost decision tree library written in Safe Rust. There is no unsafe rust code in the library.

MesaTEE GBDT-RS provides the training and inference capabilities. And it can use the models trained by xgboost to do inference tasks.

New! The MesaTEE GBDT-RS paper has been accepted by IEEE S&P'19!

Supported Task

Supppoted task for both training and inference

1. Linear regression: use SquaredError and LAD loss types

2. Binary classification (labeled with 1 and -1): use LogLikelyhood loss type

   Compatibility with xgboost

   At this time, MesaTEE GBDT-RS support to use model trained by xgboost to do inference. The model should be trained by xgboost with following configruation:

3. booster: gbtree

4. objective: "reg:linear", "reg:logistic", "binary:logistic", "binary:logitraw", "multi:softprob", "multi:softmax" or "rank:pairwise".

We have tested that MesaTEE GBDT-RS is compatible with xgboost 0.81 and 0.82

Quick Start

Training Steps

1. Set configuration
2. Load training data
3. Train the model
4. (optional) Save the model

Inference Steps

1. Load the model
2. Load the test data
3. Inference the test data

Example

    use gbdt::config::Config;
    use gbdt::decision_tree::{DataVec, PredVec};
    use gbdt::gradient_boost::GBDT;
    use gbdt::input::{InputFormat, load};

let mut cfg = Config::new();
cfg.set_feature_size(22);
cfg.set_max_depth(3);
cfg.set_iterations(50);
cfg.set_shrinkage(0.1);
cfg.set_loss("LogLikelyhood"); 
cfg.set_debug(true);
cfg.set_data_sample_ratio(1.0);
cfg.set_feature_sample_ratio(1.0);
cfg.set_training_optimization_level(2);

// load data
let train_file = "dataset/agaricus-lepiota/train.txt";
let test_file = "dataset/agaricus-lepiota/test.txt";

let mut input_format = InputFormat::csv_format();
input_format.set_feature_size(22);
input_format.set_label_index(22);
let mut train_dv: DataVec = load(train_file, input_format).expect("failed to load training data");
let test_dv: DataVec = load(test_file, input_format).expect("failed to load test data");

// train and save model
let mut gbdt = GBDT::new(&cfg);
gbdt.fit(&mut train_dv);
gbdt.save_model("gbdt.model").expect("failed to save the model");

// load model and do inference
let model = GBDT::load_model("gbdt.model").expect("failed to load the model");
let predicted: PredVec = model.predict(&test_dv);

Example code

• Linear regression: examples/iris.rs
• Binary classification: examples/agaricus-lepiota.rs

Use models trained by xgboost

Steps

1. Use xgboost to train a model
2. Use examples/convert_xgboost.py to convert the model
   • Usage: python convertxgboost.py xgboostmodelpath objective outputpath
   • Note convert_xgboost.py depends on xgboost python libraries. The converted model can be used on machines without xgboost
3. In rust code, call GBDT::loadfromxgboost(model_path, objective) to load the model
4. Do inference
5. (optional) Call GBDT::save_model to save the model to MesaTEE GBDT-RS native format.

Example code

• "reg:linear": examples/test-xgb-reg-linear.rs
• "reg:logistic": examples/test-xgb-reg-logistic.rs
• "binary:logistic": examples/test-xgb-binary-logistic.rs
• "binary:logitraw": examples/test-xgb-binary-logistic.rs
• "multi:softprob": examples/test-xgb-multi-softprob.rs
• "multi:softmax": examples/test-xgb-multi-softmax.rs
• "rank:pairwise": examples/test-xgb-rank-pairwise.rs

Multi-threading

Training:

At this time, training in MesaTEE GBDT-RS is single-threaded.

Inference:

The related inference functions are single-threaded. But they are thread-safe. We provide an inference example using multi threads in example/test-multithreads.rs

SGX usage

Because MesaTEE GBDT-RS is written in pure rust, with the help of rust-sgx-sdk, it can be used in sgx enclave easily as:

gbdt_sgx = { git = "https://github.com/mesalock-linux/gbdt-rs" }

This would import a crate named

gbdt_sgx

gbdt

gbdt = { package = "gbdt_sgx", git = "https://github.com/mesalock-linux/gbdt-rs" }

For more information and concret examples, please look at directory

sgx/gbdt-sgx-test

License

Apache 2.0

Authors

Tianyi Li @n0b0dyCN [email protected]

Tongxin Li @litongxin1991 [email protected]

Yu Ding @dingelish [email protected]

Steering Committee

Tao Wei, Yulong Zhang

Acknowledgment

Thanks to @qiyiping for his/her great previous work gbdt. We read his/her code before starting this project.