├── src ├── conftest.py ├── __init__.py └── ppxgboost │ ├── __init__.py │ ├── OPEMetadata.py │ ├── PPKey.py │ ├── PPModel.py │ ├── PaillierAPI.py │ ├── PPQuery.py │ ├── PPPrediction.py │ └── PPTree.py ├── test ├── __init__.py ├── test_files │ ├── model_file.pkl │ └── test_prediction_input.csv ├── test_PPKey.py └── test_ppxg_boost.py ├── examples ├── __init__.py ├── readmitted.ipynb ├── Titanic.ipynb └── Iris.ipynb ├── NOTICE ├── DEVELOPMENT.md ├── .gitignore ├── CODE_OF_CONDUCT.md ├── pyproject.toml ├── README.md ├── CONTRIBUTING.md ├── LICENSE └── data ├── titanic_test.csv └── titanic_train.csv /src/conftest.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /test/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /examples/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /NOTICE: -------------------------------------------------------------------------------- 1 | Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | -------------------------------------------------------------------------------- /DEVELOPMENT.md: -------------------------------------------------------------------------------- 1 | This code is tested under MacOS (>= Mac OS 10.14), and Ubuntu 18.04 2 | Python version: Python 3.7.3 3 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | -------------------------------------------------------------------------------- /test/test_files/model_file.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/awslabs/privacy-preserving-xgboost-inference/HEAD/test/test_files/model_file.pkl -------------------------------------------------------------------------------- /src/ppxgboost/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | # Implement your code here. 5 | __author__ = 'Xianrui Meng' 6 | __version__ = '0.0.1' 7 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .idea/ 3 | build/ 4 | build-tools/ 5 | .pytest_cache 6 | __pycache__/ 7 | *.egg-info/ 8 | .installed.cfg 9 | *.egg 10 | env/ 11 | develop-eggs/ 12 | dist/ 13 | *.ipynb_checkpoints/ 14 | examples/tree.txt 15 | .venv 16 | .vscode 17 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | ## Code of Conduct 2 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 3 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 4 | opensource-codeofconduct@amazon.com with any additional questions or comments. 5 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | # https://ianhopkinson.org.uk/2022/02/understanding-setup-py-setup-cfg-and-pyproject-toml-in-python/ 2 | [build-system] 3 | requires = ["setuptools"] 4 | build-backend = "setuptools.build_meta" 5 | 6 | [project] 7 | name = "ppxgboost" 8 | version = "0.0.1" 9 | dependencies = [ 10 | "numpy>=1.22.0", 11 | "pandas==1.5.3", 12 | "phe==1.5.0", 13 | "xgboost>=1.6.2", 14 | "pytest==7.2.1", 15 | "pyope==0.2.2", 16 | ] 17 | 18 | [project.optional-dependencies] 19 | examples = ["jupyter", "scikit-learn>=1.0.2",] 20 | -------------------------------------------------------------------------------- /test/test_files/test_prediction_input.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q 9 | 899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S 10 | 900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C 11 | 901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S 12 | 902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S 13 | 903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S 14 | 904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S 15 | 905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S 16 | 906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S 17 | 907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C 18 | 908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q 19 | 909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C 20 | 910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S 21 | 911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C 22 | 912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C 23 | 913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S 24 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Privacy-Preserving XGBoost Inference 2 | 3 | ## Description 4 | This repo is a prototype implementation of privacy-preserving XGBoost (https://xgboost.readthedocs.io/en/latest/). 5 | We adopt several property-preserving encryption schemes to encrypt the XGBoost model so that 6 | the privacy-preserving model can predict an encrypted query. 7 | 8 | An extended abstract of this work (https://arxiv.org/abs/2011.04789) appears in Privacy-preserving Machine Learning Workshop at NeurIPS 2020. 9 | 10 | ## Development 11 | 12 | This package requires python>=3.8. Install the dependencies with 13 | 14 | - python -m pip install . 15 | 16 | This command installs the dependencies in a specific order. 17 | 18 | Run the tests with: 19 | - cd test 20 | - python -m pytest 21 | 22 | Install dependencies required to run the examples with: 23 | - python -m pip install .[examples] 24 | Run the examples with: 25 | - cd examples 26 | - jupyter nbconvert --to notebook --execute foo.ipynb --output foo.ipynb 27 | You can clear the notebook output with 28 | - jupyter nbconvert --clear-output --ClearMetadataPreprocessor.enabled=True foo.ipynb 29 | 30 | This package depends on the Paillier partially homomorphic encryption scheme (https://en.wikipedia.org/wiki/Paillier_cryptosystem). It also includes source code for a modified version of Boldyreva et. al.'s order-preserving encryption scheme (https://github.com/tonyo/pyope). The source code is place in the 'third-party/ope/' directory. 31 | 32 | See [DEVELOPMENT.md](./DEVELOPMENT.md) 33 | 34 | ## Security 35 | 36 | See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. 37 | 38 | ## License 39 | 40 | This project is licensed under the Apache-2.0 License. 41 | -------------------------------------------------------------------------------- /test/test_PPKey.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | # This test file tests the Key wrapper (PPKey.py) 5 | from random import randrange 6 | from secrets import token_bytes 7 | from ppxgboost import PaillierAPI as paillier 8 | import ppxgboost.PPKey as PPKey 9 | import pyope.ope as pyope 10 | 11 | # The tests require modified input and output ranges 12 | in_range = pyope.ValueRange(pyope.DEFAULT_IN_RANGE_START, 2 ** 43 - 1) 13 | out_range = pyope.ValueRange(pyope.DEFAULT_OUT_RANGE_START, 2 ** 63 - 1) 14 | 15 | class Test_Key: 16 | def test_get_PPBoost_key(self): 17 | """ 18 | Testing the PPBoost Key Wrapper. 19 | """ 20 | 21 | # Build the PPModelKey 22 | ppModelKey, ppQueryKey = PPKey.generatePPXGBoostKeys(in_range, out_range) 23 | 24 | a = randrange(pow(2, 30)) 25 | b = randrange(pow(2, 30)) 26 | 27 | # test the ope key 28 | ea = ppModelKey.get_ope_encryptor().encrypt(a) 29 | eb = ppModelKey.get_ope_encryptor().encrypt(b) 30 | assert (a < b) == (ea < eb) 31 | 32 | ea = ppModelKey.get_public_key().encrypt(a) 33 | eb = ppModelKey.get_public_key().encrypt(b) 34 | assert ppQueryKey.get_private_key().decrypt(ea + eb) == a + b 35 | 36 | def test_get_private_key(self): 37 | """ 38 | Testing the Client Key Wrapper. 39 | """ 40 | 41 | # Build the PPQueryKey 42 | ppModelKey, ppQueryKey = PPKey.generatePPXGBoostKeys(in_range, out_range) 43 | 44 | a = randrange(pow(2, 30)) 45 | b = randrange(pow(2, 30)) 46 | 47 | # test the ope key 48 | ea = ppQueryKey.get_ope_encryptor().encrypt(a) 49 | eb = ppQueryKey.get_ope_encryptor().encrypt(b) 50 | assert (a < b) == (ea < eb) 51 | 52 | ea = ppModelKey.get_public_key().encrypt(a) 53 | eb = ppModelKey.get_public_key().encrypt(b) 54 | assert ppQueryKey.get_private_key().decrypt(ea + eb) == a + b 55 | -------------------------------------------------------------------------------- /src/ppxgboost/OPEMetadata.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import pyope.ope as pyope 5 | 6 | class OPEMetadata: 7 | """ 8 | OPE encryption takes floating point values in a fixed range and 9 | maps them to an integer range. This metadata defines the input 10 | range for this OPE instance. 11 | 12 | Although it would be nice if the model encryption was context-free, 13 | evaluating a model requires comparing values in an (unknown at encryption time) 14 | query to values in the model itself. Thus the input range for the OPE 15 | instance depends on the comparison values in the model itself as well 16 | as the minimum and maximum values in any query, across all features. 17 | """ 18 | 19 | def __init__(self, model, test_data_min, test_data_max, max_num_ope_enc = pyope.DEFAULT_IN_RANGE_END): 20 | """ 21 | :param model: plaintext PPXGBoost model 22 | :param test_data_min: minimum value across all features of all (future) queries 23 | :param test_data_max: maximum value across all features of all (future) queries 24 | :param max_num_ope_enc: the maximum number that the OPE encryption can support. This value is also used 25 | as the maximum output of the affine transformation 26 | """ 27 | model_min, model_max = model.get_extreme_values() 28 | self.min_val = min(test_data_min, model_min) 29 | self.max_val = max(test_data_max, model_max) 30 | self.max_num_ope_enc = max_num_ope_enc 31 | 32 | def affine_transform(self, x): 33 | """ 34 | This affine transformation will linearly rescale [min_val, max_val] to [0, max_num_ope_enc]. 35 | Linear rescaling: (x - min_val) * max_num_ope_enc / (max_val - min_val) 36 | 37 | :param x: float in range [min_val, max_val] 38 | :return: int in range [0, max_num_ope_enc] 39 | """ 40 | if x > self.max_val or x < self.min_val: 41 | raise Exception('Input ' + str(x) + ' is outside allowed range [' + 42 | str(self.min_val) + ', ' + str(self.max_val) + ']') 43 | 44 | return int((x - self.min_val) * self.max_num_ope_enc / (self.max_val - self.min_val)) 45 | -------------------------------------------------------------------------------- /src/ppxgboost/PPKey.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | from phe import PaillierPrivateKey, PaillierPublicKey 5 | from pyope.ope import OPE, ValueRange, DEFAULT_IN_RANGE_START, DEFAULT_IN_RANGE_END, DEFAULT_OUT_RANGE_START, DEFAULT_OUT_RANGE_END 6 | from secrets import token_bytes 7 | import ppxgboost.PaillierAPI as paillier 8 | 9 | 10 | class PPModelKey: 11 | """ 12 | Key used to encrypt a PPModel 13 | """ 14 | 15 | def __init__(self, public_key: PaillierPublicKey, prf_key: bytes, ope_encryptor: OPE): 16 | """ 17 | :param public_key: Paillier cryptosystem public key 18 | :param prf_key: PRF key 19 | :param ope_encryptor: OPE key 20 | """ 21 | # Private Attributes 22 | self.__public_key = public_key 23 | self.__prf_key = prf_key 24 | self.__ope_encryptor = ope_encryptor 25 | 26 | def get_public_key(self): 27 | """ 28 | :return: the Paillier public key 29 | """ 30 | return self.__public_key 31 | 32 | def get_prf_key(self): 33 | """ 34 | :return: the PRF key 35 | """ 36 | return self.__prf_key 37 | 38 | def get_ope_encryptor(self): 39 | """ 40 | :return: the OPE encrypter 41 | """ 42 | return self.__ope_encryptor 43 | 44 | 45 | class PPQueryKey: 46 | """ 47 | Key used to encrypt queries and decrypt query results 48 | """ 49 | 50 | def __init__(self, private_key: PaillierPrivateKey, prf_key: bytes, ope_encryptor: OPE): 51 | """ 52 | :param private_key: Paillier cryptosystem private key 53 | :param prf_key: PRF key 54 | :param ope_encryptor: OPE key 55 | """ 56 | # Private Attributes 57 | self.__private_key = private_key 58 | self.__prf_key = prf_key 59 | self.__ope_encryptor = ope_encryptor 60 | 61 | def get_private_key(self): 62 | """ 63 | :return: the Paillier private key 64 | """ 65 | return self.__private_key 66 | 67 | def get_prf_key(self): 68 | """ 69 | :return: the PRF key 70 | """ 71 | return self.__prf_key 72 | 73 | def get_ope_encryptor(self): 74 | """ 75 | :return: the OPE encrypter 76 | """ 77 | return self.__ope_encryptor 78 | 79 | # default OPE parameters 80 | default_input_range = ValueRange(DEFAULT_IN_RANGE_START, DEFAULT_IN_RANGE_END) 81 | default_output_range = ValueRange(DEFAULT_OUT_RANGE_START, DEFAULT_OUT_RANGE_END) 82 | 83 | def generatePPXGBoostKeys(ope_input_range = default_input_range, ope_output_range = default_output_range): 84 | """ 85 | Generate keys to encrypt an XGBoost model, encrypt queries, and decrypt query results. 86 | 87 | :return: model encryption keys, query encryption/decryption keys 88 | """ 89 | 90 | # token bytes calls the os.urandom(). 91 | prf_key = token_bytes(16) 92 | paillier_public_key, paillier_private_key = paillier.he_key_gen() 93 | ope_encryptor = OPE(token_bytes(16), ope_input_range, ope_output_range) 94 | return (PPModelKey(paillier_public_key, prf_key, ope_encryptor), PPQueryKey(paillier_private_key, prf_key, ope_encryptor)) 95 | -------------------------------------------------------------------------------- /src/ppxgboost/PPModel.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import ppxgboost.PPTree as PPTree 5 | from ppxgboost.PPKey import PPModelKey 6 | from ppxgboost.PPQuery import PPQuery 7 | import xgboost 8 | from ppxgboost.OPEMetadata import OPEMetadata 9 | 10 | class PPModel: 11 | """ 12 | A representation of an XGBoost model composed of multiple trees. 13 | """ 14 | 15 | def __init__(self, trees): 16 | """ 17 | :param trees: a list of PPTrees 18 | """ 19 | 20 | self.trees = list(trees) 21 | 22 | def eval(self, x: PPQuery): 23 | """ 24 | Evaluate the model on the given query. 25 | 26 | :param x: dictionary corresponding to the input 27 | :return: result of evaluating the model on x 28 | """ 29 | 30 | return list(map(lambda t: t.eval(x), self.trees)) 31 | 32 | def get_features(self): 33 | """ 34 | :return: The set of features used by this model 35 | """ 36 | 37 | features = set() 38 | for t in self.trees: 39 | features = features.union(t.get_features()) 40 | return features 41 | 42 | def get_extreme_values(self): 43 | """ 44 | :return: The minimum and maximum comparison values in the model 45 | """ 46 | 47 | min_val = float('inf') 48 | max_val = float('-inf') 49 | for t in self.trees: 50 | t_min, t_max = t.get_extreme_values() 51 | min_val = min(min_val, t_min) 52 | max_val = max(max_val, t_max) 53 | return min_val, max_val 54 | 55 | def encrypt(self, pp_boost_key: PPModelKey, metadata: OPEMetadata): 56 | """ 57 | Encrypt a plaintext XGBoost model 58 | 59 | :param pp_boost_key: The model encryption key 60 | :param metadata: OPE metadata 61 | :return: a new PPModel corresponding to the encryption of `self` 62 | """ 63 | 64 | # use a global dictionary so that we only encrypt each feature once 65 | global_feature_encryption_dict = {} 66 | return PPModel(map(lambda t: t.encrypt(pp_boost_key, metadata, global_feature_encryption_dict), self.trees)) 67 | 68 | 69 | def from_xgboost_model(model: xgboost.core.Booster) -> PPModel: 70 | """ 71 | Create a PPModel from an xgboost library model. 72 | 73 | :param model: an XGBoost model from the xgboost library 74 | :return: a PPModel corresponding to the input model 75 | """ 76 | 77 | # xgboost doesn't provide a way to access the model parameters directly, 78 | # so we convert to the PPModel representation via serialization. 79 | # Specifically, xgboost will return a string representation of the model, 80 | # which we then convert to a PPModel using PPTree deserialization. This 81 | # works because the PPTree serialization was designed to coincide with 82 | # xgboost serialization of a model. 83 | 84 | # get_dump() returns a list strings, each representing a single tree in the model 85 | # We parse this string representation one tree at a time to create 86 | # an internal representation of the model. 87 | trees_dump = model.get_dump() 88 | 89 | # For each string representation of an xgboost tree, parse the representation 90 | # into an internal representation of the tree 91 | output_trees = map(PPTree.parse_tree, trees_dump) 92 | 93 | # output an (internal) XGBoost model 94 | return PPModel(output_trees) 95 | -------------------------------------------------------------------------------- /src/ppxgboost/PaillierAPI.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import json 5 | import phe 6 | 7 | 8 | # Wrapper of the Paillier encryption scheme. 9 | def he_key_gen(private_keyring=None, n_length=phe.paillier.DEFAULT_KEYSIZE): 10 | """ 11 | Key generation of the Paillier homomorphic encryption. The key_gen can take 12 | a private_key and n_length as parameters. The default key length is 2048 13 | :param private_keyring: this parameter is the private key that used to 14 | generate the public key. We usually do not set up this parameter. 15 | :param n_length: the key length -- the default key size is set to 2048. 16 | :return: public/private key pair 17 | """ 18 | 19 | public_key, private_key = phe.generate_paillier_keypair(private_keyring, n_length) 20 | return public_key, private_key 21 | 22 | def encrypt(public_key, msg, precision=None): 23 | """ 24 | Encode and Paillier encrypt a real number *msg*. 25 | 26 | Args: 27 | msg: an int or float to be encrypted. If int, it must satisfy abs(*msg*) < public_key.n / 3. 28 | If float, it must satisfy abs(*msg* / *precision*) << n / 3 29 | (i.e. if a float is near the limit then detectable 30 | overflow may still occur) 31 | precision (float): Passed to :method:`EncodedNumber.encode`. 32 | If *msg* is a float then *precision* is the maximum 33 | **absolute** error allowed when encoding *msg*. Defaults 34 | to encoding *msg* exactly. 35 | 36 | :param public_key: public key 37 | :param msg: message 38 | :param precision: this is optional, the maximum absolute error allowed when encoding *msg* 39 | :return: Enc(message) 40 | """ 41 | return public_key.encrypt(msg, precision) 42 | 43 | 44 | def decrypt(private_key, msg): 45 | """ 46 | private_key to decrypt the message 47 | :param private_key: private key 48 | :param msg: encrypted number 49 | :return: decrypt and decode the number 50 | """ 51 | return private_key.decrypt(msg) 52 | 53 | 54 | def assert_ciphertext(encrypted_number): 55 | """ 56 | Check if *encrypted_number* is a valid EncryptedNumber type 57 | :param encrypted_number: input 58 | exit if it is not. 59 | """ 60 | if not isinstance(encrypted_number, phe.EncryptedNumber): 61 | raise ValueError("encrypted input is not a EncryptedNumber Type") 62 | 63 | 64 | def ciphertext_serialization(public_key, encrypted_number): 65 | """ 66 | EncryptedNumber: 67 | ciphertext (int) – encrypted representation of the encoded number. 68 | exponent (int) – used by EncodedNumber to keep track of fixed precision. Usually negative. 69 | Exponent will be zero when encrypting an integer. 70 | :param public_key: the public key 71 | :param encrypted_number: encrypted ciphertext 72 | :return: 73 | """ 74 | encrypt_with_pub_key = {'public_key': {'n': public_key.n}, 75 | 'values': [str(encrypted_number.ciphertext()), encrypted_number.exponent]} 76 | serialised = json.dumps(encrypt_with_pub_key) 77 | return serialised 78 | 79 | 80 | def ciphertext_deserialization(serialised): 81 | """ 82 | EncryptedNumber: ciphertext (int), exponent (int). 83 | :param serialised json format 84 | :return: public key and the ciphertext. 85 | """ 86 | received_dict = json.loads(serialised) 87 | pk = received_dict['public_key'] 88 | public_key_rec = phe.paillier.PaillierPublicKey(n=int(pk['n'])) 89 | enc_nums_rec = phe.paillier.EncryptedNumber(public_key_rec, int(received_dict['values'][0]), 90 | int(received_dict['values'][1])) 91 | return public_key_rec, enc_nums_rec 92 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing Guidelines 2 | 3 | Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional 4 | documentation, we greatly value feedback and contributions from our community. 5 | 6 | Please read through this document before submitting any issues or pull requests to ensure we have all the necessary 7 | information to effectively respond to your bug report or contribution. 8 | 9 | 10 | ## Security 11 | This work is research prototype and it has not been used or tested in production environment. Privacy-preserving XGBoost uses a non-standard security model. 12 | Unlike a traditional security model (such as [semantic security](https://en.wikipedia.org/wiki/Semantic_security)), the model operator learns information about clients' encrypted queries (for instance, the relative order, i.e encrypted value x > encrypted value y). 13 | The model operator might also learn how one encrypted query might be related to another. In particular, the model operator can detect whether the encrypted response is the same. 14 | We recommend users read the detailed security analysis in our paper (https://arxiv.org/abs/2011.04789). 15 | 16 | ## Reporting Bugs/Feature Requests 17 | 18 | We welcome you to use the GitHub issue tracker to report bugs or suggest features. 19 | 20 | When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already 21 | reported the issue. Please try to include as much information as you can. Details like these are incredibly useful: 22 | 23 | * A reproducible test case or series of steps 24 | * The version of our code being used 25 | * Any modifications you've made relevant to the bug 26 | * Anything unusual about your environment or deployment 27 | 28 | 29 | ## Contributing via Pull Requests 30 | Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that: 31 | 32 | 1. You are working against the latest source on the *master* branch. 33 | 2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already. 34 | 3. You open an issue to discuss any significant work - we would hate for your time to be wasted. 35 | 36 | To send us a pull request, please: 37 | 38 | 1. Fork the repository. 39 | 2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change. 40 | 3. Ensure local tests pass. 41 | 4. Commit to your fork using clear commit messages. 42 | 5. Send us a pull request, answering any default questions in the pull request interface. 43 | 6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation. 44 | 45 | GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and 46 | [creating a pull request](https://help.github.com/articles/creating-a-pull-request/). 47 | 48 | 49 | ## Finding contributions to work on 50 | Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start. 51 | 52 | 53 | ## Code of Conduct 54 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 55 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 56 | opensource-codeofconduct@amazon.com with any additional questions or comments. 57 | 58 | 59 | ## Security issue notifications 60 | If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue. 61 | 62 | 63 | ## Licensing 64 | 65 | See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution. 66 | 67 | We may ask you to sign a [Contributor License Agreement (CLA)](http://en.wikipedia.org/wiki/Contributor_License_Agreement) for larger changes. 68 | -------------------------------------------------------------------------------- /src/ppxgboost/PPQuery.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import math 5 | import pandas as pd 6 | import base64 7 | import encodings 8 | import hashlib 9 | import hmac 10 | 11 | from ppxgboost.OPEMetadata import * 12 | from ppxgboost.PPKey import PPQueryKey 13 | 14 | def hmac_msg(prf_key_hash: bytes, feature): 15 | """ 16 | HMAC of a string using the provided key 17 | 18 | :param prf_key_hash: hash key as bytes 19 | :param feature: feature name as a string (encoded using 'UTF-8') 20 | :return: HMAC of feature name 21 | """ 22 | 23 | # The encryption scheme requires a PRF to create 'pseudonyms' for the features. 24 | # We instantiate the PRF with HMAC using utf-8 encoding (python3) for feature names. 25 | # A reference that shows HMAC is a PRF (see Theorem 1 in https://eprint.iacr.org/2014/578.pdf) 26 | message = bytes(feature, encodings.utf_8.getregentry().name) 27 | sig = base64.b64encode(hmac.new(prf_key_hash, message, hashlib.sha256).digest()) 28 | return sig.decode() 29 | 30 | class PPQuery: 31 | """ 32 | PPXGBoost query object 33 | """ 34 | 35 | def __init__(self, query_dict): 36 | """ 37 | :param query_dict: A dictionary containing the query labels and values 38 | """ 39 | self.query_dict = query_dict 40 | 41 | class QueryEncryptor: 42 | """ 43 | Encryptor object for queries 44 | """ 45 | 46 | def __init__(self, query_key: PPQueryKey, feature_set, metadata: OPEMetadata): 47 | """ 48 | :param query_key: The query encryption/decryption key 49 | :param feature_set: The set of model features to encrypt in a query 50 | :param metadata: OPE metadata 51 | """ 52 | 53 | self.key = query_key 54 | self.feature_set = feature_set 55 | self.metadata = metadata 56 | self.feature_name_map = {} 57 | for f in self.feature_set: 58 | self.feature_name_map[f] = hmac_msg(self.key.get_prf_key(), f) 59 | 60 | def encrypt_query(self, query: PPQuery): 61 | """ 62 | Encrypt a single query by HMACing each feature name and OPE-encrypting 63 | each value. 64 | 65 | :param query: An unencrypted PPQuery 66 | :return: An encrypted PPQuery 67 | """ 68 | 69 | encrypted_query = {} 70 | for k, v in query.query_dict.items(): 71 | if k not in self.feature_set: 72 | continue 73 | 74 | # TODO: datasets in the tests *do* contain NaN 75 | # but this leaks where NaNs are... 76 | if math.isnan(v): 77 | encrypted_val = v 78 | else: 79 | normalized_val = self.metadata.affine_transform(v) 80 | 81 | if normalized_val > self.metadata.max_num_ope_enc or normalized_val < 0: 82 | raise Exception("Invalid input: input is out of range (0, " + str(self.metadata.max_num_ope_enc) + 83 | "). The system cannot encrypt", normalized_val) 84 | encrypted_val = self.key.get_ope_encryptor().encrypt(int(normalized_val)) 85 | encrypted_query[self.feature_name_map[k]] = encrypted_val 86 | return PPQuery(encrypted_query) 87 | 88 | def encrypt_queries(encryptor: QueryEncryptor, queries: [PPQuery]): 89 | return list(map(lambda q: encryptor.encrypt_query(q), queries)) 90 | 91 | def pandas_to_queries(data_set: pd.DataFrame): 92 | """ 93 | Convert data parsed with pandas into a list of queries 94 | 95 | :param data_set: A pandas data frame 96 | :return: list of queries 97 | """ 98 | 99 | queries = [] 100 | for i, row in data_set.iterrows(): 101 | query = {} 102 | for feature in list(data_set.columns.values): 103 | query[feature] = row[feature] 104 | queries.append(PPQuery(query)) 105 | return queries 106 | 107 | # pytest will try to do weird things if you have a function name that starts with `test_`!! 108 | # "fixture 'test_data' not found" 109 | def get_test_data_extreme_values(queries: [PPQuery]): 110 | """ 111 | Extract the minimum and maximum values from a list of queries. 112 | 113 | The `OPEMetadata` class requires an estimate for the minimum and maximum 114 | values across all features and all queries. For tests and examples, 115 | the entire test dataset is known in advance, so we can just extract 116 | the minimum and maximum values from the dataset directly. 117 | 118 | :param test_data: A non-empty list of queries 119 | :return: minimum value across all queries, maximum value across all queries 120 | """ 121 | 122 | def query_extremes(q: PPQuery): 123 | values = q.query_dict.values() 124 | return min(values), max(values) 125 | 126 | extremes = map(query_extremes, queries) 127 | # unpack the list of tuples into mins and maxes 128 | q_mins, q_maxs = list(zip(*extremes)) 129 | 130 | return min(q_mins), max(q_maxs) 131 | -------------------------------------------------------------------------------- /src/ppxgboost/PPPrediction.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import numpy as np 5 | import pandas as pd 6 | 7 | import ppxgboost.PaillierAPI as paillier 8 | from ppxgboost.PPModel import * 9 | from ppxgboost.PPQuery import PPQuery 10 | 11 | 12 | def predict_single_input_binary(model: PPModel, query, default_base_score=0.5): 13 | """ 14 | Return the score for this query in a binary classification model 15 | 16 | :param model: the model to evaluate 17 | :param query: a single input query 18 | :param default_base_score: a default score is 0.5 (global bias) 19 | :return: the query's score 20 | """ 21 | 22 | scores = model.eval(query) 23 | return default_base_score + sum(scores) 24 | 25 | def predict_binary(enc_model: PPModel, enc_queries: [PPQuery], default_base_score=0.5): 26 | """ 27 | Prediction on @enc_queries over the @enc_model 28 | :param enc_model: encrypted PPModel 29 | :param enc_queries: a list of encrypted PPQuery 30 | :param default_base_score: default score is 0.5 (according to the xgboost -- global bias) 31 | :return: a list of prediction values 32 | """ 33 | 34 | return list(map(lambda q: predict_single_input_binary(enc_model, q, default_base_score), enc_queries)) 35 | 36 | 37 | def predict_single_input_multiclass(model: PPModel, num_classes, query): 38 | """ 39 | Return a prediction on a single input vector. 40 | The algorithm computes the sum of scores for all the corresponding classes (boosters in the xgboost model). 41 | For each class, it sum up all the leaves' values 42 | 43 | :param model: internal model representation 44 | :param num_classes: the total number classes to classify 45 | :param query: a single input query 46 | :return: the predicted score 47 | """ 48 | scores = model.eval(query) 49 | 50 | # sum of score for each category: exp(score) 51 | result = [] 52 | for i in range(num_classes): 53 | result.append(0) 54 | 55 | # this is to compute the softmax, however, server can only perform 56 | # additvely homomorphic operation, so here we can compute scores 57 | # seperately 58 | for i in range(len(scores)): 59 | j = i % num_classes 60 | result[j] = result[j] + scores[i] 61 | 62 | return result 63 | 64 | def predict_multiclass(enc_model: PPModel, num_classes, enc_queries: [PPQuery]): 65 | """ 66 | This prediction for dataframe input. For each record, 67 | it calls the 'predict_single_input_multiclass' 68 | :param enc_model: encrypted PPModel 69 | :param num_classes: the total number classes to classify 70 | :param enc_queries: list of encrypted PPQuery 71 | :return: a list of prediction with aggregated scores 72 | """ 73 | 74 | return list(map(lambda q: predict_single_input_multiclass(enc_model, num_classes, q), enc_queries)) 75 | 76 | def client_side_multiclass_compute(predictions): 77 | """ 78 | Output the actual predictions using the softmax methods. 79 | In particular, it computes the normalized exponential function, which converts a vector 80 | of K real numbers and normalizes it into a probability distribution consisting of K 81 | probability scores proportional to the exponentials of the input numbers, i.e. SoftMax assigns 82 | decimal probabilities to each class in a multi-class problem. 83 | Those decimal probabilities must add up to 1.0. 84 | 85 | :param predictions: a list of predictions, where each element is a list that contains 86 | the scores for each inputs - here, we allow the client to receive a list of 87 | prediction (consistent to xgboost's prediction) for the corresponding queries. 88 | :return: predicted classes: a list of the most probable classes the model predicts. 89 | """ 90 | final_output = [] 91 | 92 | # Predictions is a vector of results, the value can be any real floating point number 93 | # e.g. predictions for k classes -- predictions = [[x_1,... x_k], [y_1,... y_k], [z_1, ..., z_k]] 94 | for predict_i in predictions: 95 | 96 | # gets the sum of all the exponential predicted results 97 | # e.g. e^(x_1) + ... + e^(x_k) 98 | sum_score = np.sum(np.exp(predict_i)) 99 | output = [] 100 | for x_i in predict_i: 101 | # for each t in predict_i 102 | # e.g. e^(x_i)/(e^(x_1) + ... + e^(x_k)) 103 | output.append(np.exp(x_i) / sum_score) 104 | 105 | # report the argmax as the predicted class (most probable). 106 | final_output.append(np.argmax(output)) 107 | return final_output 108 | 109 | 110 | def client_decrypt_prediction_multiclass(private_key, predictions): 111 | """ 112 | Client calls this function to compute the multi-class prediction -- i.e. it outputs the most probable class for the 113 | predicted class. 114 | This methods first decrypts the aggregated predictions using @private_key@, 115 | then calls the client_side_multiclass_compute() function. 116 | 117 | :param private_key: the private key to decrypt the values. 118 | :param predictions: encrypted list of predictions (each entry of the list is a list of encrypted scores.) 119 | :return: the results as numpy.ndarray() 120 | """ 121 | decrypted_pred_list = [] 122 | for enc_pred in predictions: 123 | decrypted_scores = [] 124 | for enc_scores in enc_pred: 125 | # decrypts the encrypted scores. 126 | decrypted_scores.append(paillier.decrypt(private_key, enc_scores)) 127 | decrypted_pred_list.append(decrypted_scores) 128 | result = client_side_multiclass_compute(decrypted_pred_list) 129 | return np.array(result) 130 | -------------------------------------------------------------------------------- /examples/readmitted.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": { 7 | "tags": [] 8 | }, 9 | "source": [ 10 | "### Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n", 11 | "### SPDX-License-Identifier: Apache-2.0" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "( Run jupyter notebook under the project directory )" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "from ppxgboost import PPPrediction as prediction\n", 28 | "import ppxgboost.PPModel as PPModel\n", 29 | "from ppxgboost import PaillierAPI as paillier\n", 30 | "import ppxgboost.OPEMetadata as OPEMetadata\n", 31 | "import ppxgboost.PPKey as PPKey\n", 32 | "import ppxgboost.PPQuery as PPQuery\n", 33 | "import random\n", 34 | "import time\n", 35 | "import pandas as pd\n", 36 | "import numpy as np\n", 37 | "import xgboost as xgb\n", 38 | "from xgboost import XGBClassifier\n", 39 | "from secrets import token_bytes\n", 40 | "from pyope.ope import OPE\n", 41 | "from sklearn.model_selection import train_test_split" 42 | ] 43 | }, 44 | { 45 | "attachments": {}, 46 | "cell_type": "markdown", 47 | "metadata": { 48 | "pycharm": { 49 | "name": "#%% md\n" 50 | } 51 | }, 52 | "source": [ 53 | "# XGBoost for Dataset\n", 54 | "\n", 55 | "This example demonstrates how to use ppxgboost to encrypt an xgboost model and query it.\n", 56 | "\n", 57 | "The sample dataset we are going to use in this lab is a sampled version of the \"Diabetes 130-US hospitals for years 1999-2008 Data Set\" (Beata Strack, Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura, Krzysztof J. Cios, and John N. Clore, “Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records,” BioMed Research International, vol. 2014, Article ID 781670, 11 pages, 2014. ).\n" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": { 63 | "pycharm": { 64 | "name": "#%% md\n" 65 | } 66 | }, 67 | "source": [ 68 | "### Data Preparation and Train an XGBoost ML model" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": null, 74 | "metadata": {}, 75 | "outputs": [], 76 | "source": [ 77 | "# In the following example, the datasets modified to match the input requirements by SageMaker Data Wrangler.\n", 78 | "data = pd.read_csv('../data/readmitted.csv')\n", 79 | "\n", 80 | "train, test = train_test_split(data, train_size = 0.998, test_size = 0.002)\n", 81 | "\n", 82 | "AttributeLabels = ['race', 'gender', 'age', 'time_in_hospital', 'num_lab_procedures', 'num_procedures', 'num_medications', 'number_outpatient', 'number_emergency', 'number_inpatient', 'number_diagnoses', 'max_glu_serum', 'a1c_result', 'change', 'diabetes_med']\n", 83 | "\n", 84 | "# Training dataset\n", 85 | "X_train = train[AttributeLabels]\n", 86 | "Y_train = train[['readmitted']]\n", 87 | "\n", 88 | "# Testing dataset\n", 89 | "X_test = test[AttributeLabels]\n", 90 | "Y_test = test[['readmitted']]\n", 91 | "\n", 92 | "X_train.head()" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": null, 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [ 101 | "Y_train.head()" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "# Train a xgboost model \n", 111 | "dtrain = xgb.DMatrix(X_train, label=Y_train)\n", 112 | "num_class = 3\n", 113 | "params = {'eta': 0.1, 'objective':'multi:softmax', 'num_class': num_class}\n", 114 | "model = xgb.train(params=params, dtrain=dtrain)\n", 115 | "\n", 116 | "# predict using the plaintext prediction\n", 117 | "start = time.time()\n", 118 | "plaintext_predict = model.predict(xgb.DMatrix(X_test))\n", 119 | "end = time.time()\n", 120 | "print(\"XGBoost Prediction : Elapsed Time: \", end - start)" 121 | ] 122 | }, 123 | { 124 | "attachments": {}, 125 | "cell_type": "markdown", 126 | "metadata": { 127 | "pycharm": { 128 | "name": "#%% md\n" 129 | } 130 | }, 131 | "source": [ 132 | "### Encryption Preparation for XGBoost Model\n", 133 | "\n", 134 | "1. Set up the encryption materials\n", 135 | "2. process the tree into ope_enc_tree\n", 136 | "3. Encrypts the input vector for prediction\n", 137 | "4. Perform the prediction\n", 138 | "5. Decrypt the prediction" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": null, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "# parse the tree\n", 148 | "# (add fake metadata here as this testing only test the model correctness)\n", 149 | "test_input_vector = pd.DataFrame(X_test)\n", 150 | "ppModel = PPModel.from_xgboost_model(model)\n", 151 | "features = ppModel.get_features()\n", 152 | "metadata = OPEMetadata.OPEMetadata(ppModel, 0, 132)\n", 153 | "\n", 154 | "# 1. Set up encryption materials.\n", 155 | "ppModelKey, ppQueryKey = PPKey.generatePPXGBoostKeys()\n", 156 | "\n", 157 | "# 2. process the tree into ope_enc_tree\n", 158 | "enc_model = ppModel.encrypt(ppModelKey, metadata)\n", 159 | "\n", 160 | "# 3. Encrypts the input vector for prediction (using prf_key_hash and ope-encrypter) based on the feature set.\n", 161 | "queryEncryptor = PPQuery.QueryEncryptor(ppQueryKey, features, metadata)\n", 162 | "queries = PPQuery.pandas_to_queries(test_input_vector)\n", 163 | "enc_queries = PPQuery.encrypt_queries(queryEncryptor, queries)\n", 164 | "\n", 165 | "# # 4. OPE evaluation based on OPE encrypted values in the tree nodes.\n", 166 | "start = time.time()\n", 167 | "enc_predictions = prediction.predict_multiclass(enc_model, num_class, enc_queries)\n", 168 | "end = time.time()\n", 169 | "print(\"PPXGBoost Prediction : Elapsed Time: \", end - start)\n", 170 | "\n", 171 | "# 5. Client decryption.\n", 172 | "result = prediction.client_decrypt_prediction_multiclass(ppQueryKey.get_private_key(), enc_predictions)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": null, 178 | "metadata": {}, 179 | "outputs": [], 180 | "source": [ 181 | "\n", 182 | "result = np.array([round(x, 7) for x in result])\n", 183 | "assert len(plaintext_predict) == len(result)\n", 184 | "\n", 185 | "# check if the predicted values are same (the ppxgboost might not produce same values \n", 186 | "# as the plaintext value due to precision)\n", 187 | "for i in range(len(plaintext_predict)):\n", 188 | " assert abs(plaintext_predict[i] - result[i]) < 0.000001\n" 189 | ] 190 | } 191 | ], 192 | "metadata": { 193 | "language_info": { 194 | "name": "python" 195 | } 196 | }, 197 | "nbformat": 4, 198 | "nbformat_minor": 4 199 | } 200 | -------------------------------------------------------------------------------- /examples/Titanic.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n", 10 | "# SPDX-License-Identifier: Apache-2.0" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "( Run jupyter notebook under the project directory )" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": null, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "from ppxgboost import PPPrediction as prediction\n", 27 | "import ppxgboost.PPModel as PPModel\n", 28 | "from ppxgboost import PaillierAPI as paillier\n", 29 | "import ppxgboost.OPEMetadata as OPEMetadata\n", 30 | "import ppxgboost.PPKey as PPKey\n", 31 | "import ppxgboost.PPQuery as PPQuery\n", 32 | "import random\n", 33 | "import time\n", 34 | "import pandas as pd\n", 35 | "import numpy as np\n", 36 | "import xgboost as xgb\n", 37 | "from xgboost import XGBClassifier\n", 38 | "from secrets import token_bytes\n", 39 | "import pyope.ope as pyope" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": { 45 | "pycharm": { 46 | "name": "#%% md\n" 47 | } 48 | }, 49 | "source": [ 50 | "# XGBoost for Titanic Dataset\n", 51 | "\n", 52 | "(We use this example to demenstrate how to use ppxgboost for encypting an xgboost model and query it.)\n", 53 | "\n", 54 | "Please go to https://www.kaggle.com/c/titanic/data and download the dataset.\n", 55 | "In the following example, the datasets are downloaded in the example directory\n" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": { 61 | "pycharm": { 62 | "name": "#%% md\n" 63 | } 64 | }, 65 | "source": [ 66 | "### Data Preparation and Train an XGBoost ML model" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": null, 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "# The pp-xgboost for titanic \n", 76 | "# In the following example, the datasets are downloaded in the example directory\n", 77 | "train = pd.read_csv('../data/titanic_train.csv')\n", 78 | "test = pd.read_csv('../data/titanic_test.csv')\n", 79 | "\n", 80 | "# Training dataset. We skip the data exploration part ...\n", 81 | "# Only get the features that are useful for building the ML model\n", 82 | "X_train = train[['Pclass', 'Age', 'Fare', 'SibSp', 'Parch']]\n", 83 | "y_train = train[['Survived']]\n", 84 | "\n", 85 | "# Testing dataset\n", 86 | "X_test = test[['Pclass', 'Age', 'Fare', 'SibSp', 'Parch']]\n", 87 | "\n", 88 | "X_train.head()" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "y_train.head()" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": null, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [ 106 | "X_test.head()" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": null, 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "# Train a xgboost model \n", 116 | "dtrain = xgb.DMatrix(X_train, label=y_train)\n", 117 | "params = {'eta': 0.1}\n", 118 | "model = xgb.train(params=params, dtrain=dtrain)\n", 119 | "\n", 120 | "# predict using the plaintext prediction\n", 121 | "plaintext_predict = model.predict(xgb.DMatrix(X_test))" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "Dump the model" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "model.dump_model('tree.txt')" 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "metadata": { 143 | "pycharm": { 144 | "name": "#%% md\n" 145 | } 146 | }, 147 | "source": [ 148 | "### Encryption Preparation for XGBoost Model\n", 149 | "1. Set up some metadata information for the dataset.\n", 150 | "2. Set up the encryption materials\n", 151 | "3. Encrypt the model\n", 152 | "4. Encrypt the query\n", 153 | "5. Perform the prediction\n", 154 | "6. Decrypt the prediction" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": null, 160 | "metadata": {}, 161 | "outputs": [], 162 | "source": [ 163 | "# 0. Create custom data ranges\n", 164 | "in_range = pyope.ValueRange(pyope.DEFAULT_IN_RANGE_START, 2 ** 43 - 1)\n", 165 | "out_range = pyope.ValueRange(pyope.DEFAULT_OUT_RANGE_START, 2 ** 63 - 1)\n", 166 | "\n", 167 | "# 1. parsing to internal tree data structure, and output feature set\n", 168 | "queries = PPQuery.pandas_to_queries(X_test)\n", 169 | "qmin, qmax = PPQuery.get_test_data_extreme_values(queries)\n", 170 | "ppModel = PPModel.from_xgboost_model(model)\n", 171 | "\n", 172 | "# 2. Set up encryption materials.\n", 173 | "ppModelKey, ppQueryKey = PPKey.generatePPXGBoostKeys(in_range, out_range)\n", 174 | "\n", 175 | "# 3. process the tree into ppmodel\n", 176 | "metadata = OPEMetadata.OPEMetadata(ppModel, qmin, qmax, in_range.end)\n", 177 | "features = ppModel.get_features()\n", 178 | "\n", 179 | "enc_model = ppModel.encrypt(ppModelKey, metadata)\n", 180 | "\n", 181 | "# 4. Encrypts the input vector for prediction (using prf_key_hash and ope-encrypter) based on the feature set.\n", 182 | "queryEncryptor = PPQuery.QueryEncryptor(ppQueryKey, features, metadata)\n", 183 | "enc_queries = PPQuery.encrypt_queries(queryEncryptor, queries)" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": null, 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "# 5. privacy-preserving evaluation.\n", 193 | "start = time.time()\n", 194 | "values = prediction.predict_binary(enc_model, enc_queries)\n", 195 | "end = time.time()\n", 196 | "print(\"Elapsed Time: \", end - start)" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": null, 202 | "metadata": {}, 203 | "outputs": [], 204 | "source": [ 205 | "# 6. decryption\n", 206 | "decryptions = []\n", 207 | "\n", 208 | "for c in values:\n", 209 | " decryptions.append(paillier.decrypt(ppQueryKey.get_private_key(), c))\n", 210 | "\n", 211 | "decryptions = np.array([round(x, 7) for x in decryptions])\n", 212 | "assert len(plaintext_predict) == len(decryptions)\n", 213 | "\n", 214 | "# if the predicted values are same (the ppxgboost might not produce same values \n", 215 | "# as the plaintext value due to precision)\n", 216 | "for i in range(len(plaintext_predict)):\n", 217 | " assert abs(plaintext_predict[i] - decryptions[i]) < 0.000001" 218 | ] 219 | } 220 | ], 221 | "metadata": { 222 | "language_info": { 223 | "name": "python" 224 | } 225 | }, 226 | "nbformat": 4, 227 | "nbformat_minor": 2 228 | } 229 | -------------------------------------------------------------------------------- /examples/Iris.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "collapsed": true, 7 | "pycharm": { 8 | "name": "#%% md\n" 9 | } 10 | }, 11 | "source": [ 12 | "Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n", 13 | "SPDX-License-Identifier: Apache-2.0" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": {}, 19 | "source": [ 20 | "( Run jupyter notebook under the project directory )\n", 21 | "\n", 22 | "# XGBoost for Iris Dataset\n", 23 | "\n", 24 | "We use this example to demenstrate how to use ppxgboost for encypting an xgboost model for multi-class\n", 25 | " prediction. We directly use the iris data from Sklearn, but one\n", 26 | " can go to https://archive.ics.uci.edu/ml/datasets/iris to download the original dataset.\n" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "import pandas as pd\n", 36 | "import numpy as np\n", 37 | "import xgboost as xgb\n", 38 | "from secrets import token_bytes\n", 39 | "\n", 40 | "from sklearn.datasets import load_iris\n", 41 | "from sklearn.model_selection import train_test_split\n", 42 | "\n", 43 | "from ppxgboost import PPPrediction as prediction\n", 44 | "import ppxgboost.OPEMetadata as OPEMetadata\n", 45 | "import ppxgboost.PPModel as PPModel\n", 46 | "import ppxgboost.PPKey as PPKey\n", 47 | "import ppxgboost.PPQuery as PPQuery\n", 48 | "import pyope.ope as pyope\n", 49 | "from ppxgboost import PaillierAPI as paillier" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "iris = load_iris()\n", 59 | "X = iris.data\n", 60 | "y = iris.target\n", 61 | "\n", 62 | "# Pre-assign the column name first.\n", 63 | "# the default feature name from the xgboost -- iris have 4 columns\n", 64 | "feature_names = ['f0', 'f1', 'f2', 'f3']\n", 65 | "X = pd.DataFrame(X, columns=feature_names)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": null, 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "X.head()" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": null, 80 | "metadata": {}, 81 | "outputs": [], 82 | "source": [ 83 | "y" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": null, 89 | "metadata": {}, 90 | "outputs": [], 91 | "source": [ 92 | "# splitting the data into train and test\n", 93 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n", 94 | "\n", 95 | "test_input_vector = pd.DataFrame(X_test, columns=feature_names)" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": null, 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "# first dump and pickled the model in the file directory.\n", 105 | "# total number of tree = total_estimators * number_labels\n", 106 | "# e.g. for the imported iris dataset, the number of classes is 3.\n", 107 | "\n", 108 | "# Just provide estimator number for testing purposes.\n", 109 | "total_estimaters = 6\n", 110 | "model = xgb.XGBClassifier(n_estimators=total_estimaters, objective='multi:softmax')\n", 111 | "model.fit(X, y)" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": null, 117 | "metadata": {}, 118 | "outputs": [], 119 | "source": [ 120 | "# Get the number of classes -- i.e. 3 from iris dataset\n", 121 | "# The classes as array can be get by calling model.classes_\n", 122 | "num_classes = model.n_classes_\n", 123 | "\n", 124 | "# Create custom data ranges\n", 125 | "in_range = pyope.ValueRange(pyope.DEFAULT_IN_RANGE_START, 2 ** 43 - 1)\n", 126 | "out_range = pyope.ValueRange(pyope.DEFAULT_OUT_RANGE_START, 2 ** 63 - 1)\n", 127 | "\n", 128 | "# parse the tree\n", 129 | "ppModel = PPModel.from_xgboost_model(model.get_booster())\n", 130 | "features = ppModel.get_features()\n", 131 | "# (add fake test data range here as this testing only test the model correctness)\n", 132 | "metadata = OPEMetadata.OPEMetadata(ppModel, 0, 100, in_range.end)" 133 | ] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "metadata": { 138 | "pycharm": { 139 | "name": "#%% md\n" 140 | } 141 | }, 142 | "source": [ 143 | "Encryption Preparation for XGBoost Model\n", 144 | "1). Set up some metadata information for the dataset.\n", 145 | "2). Set up the encryption materials\n", 146 | "3). Encrypt the model\n", 147 | "4). Encrypt the query\n", 148 | "5). Perform the prediction \n", 149 | "6). Decrypt the prediction" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": null, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "# ##################################################################################\n", 159 | "# # The folowing is to compute the scores based on the OPE processed decision tree #\n", 160 | "# ##################################################################################\n", 161 | "# # Set up encryption materials.\n", 162 | "ppModelKey, ppQueryKey = PPKey.generatePPXGBoostKeys(in_range, out_range)\n", 163 | "\n", 164 | "# 1. process the tree into ope_enc_tree\n", 165 | "enc_model = ppModel.encrypt(ppModelKey, metadata)\n" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "execution_count": null, 171 | "metadata": {}, 172 | "outputs": [], 173 | "source": [ 174 | "# 2. Encrypts the input vector for prediction (using prf_key_hash and ope-encrypter) based on the feature set.\n", 175 | "queryEncryptor = PPQuery.QueryEncryptor(ppQueryKey, features, metadata)\n", 176 | "queries = PPQuery.pandas_to_queries(test_input_vector)\n", 177 | "enc_queries = PPQuery.encrypt_queries(queryEncryptor, queries)" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": { 183 | "pycharm": { 184 | "name": "#%% md\n" 185 | } 186 | }, 187 | "source": [ 188 | "Note that: The prediction on the server side is done differently from the log:binary. This is because\n", 189 | "the server needs to perofrm the softmax aggregation." 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": null, 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "# # 3. OPE evaluation based on OPE encrypted values in the tree nodes.\n", 199 | "enc_predictions = prediction.predict_multiclass(enc_model, num_classes, enc_queries)" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": null, 205 | "metadata": {}, 206 | "outputs": [], 207 | "source": [ 208 | "# 4. Client decryption.\n", 209 | "result = prediction.client_decrypt_prediction_multiclass(ppQueryKey.get_private_key(), enc_predictions)" 210 | ] 211 | }, 212 | { 213 | "cell_type": "code", 214 | "execution_count": null, 215 | "metadata": {}, 216 | "outputs": [], 217 | "source": [ 218 | "real_y = model.predict(X_test)\n", 219 | "assert np.array_equal(result, real_y)\n", 220 | "print(\"success!\")\n" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [] 229 | } 230 | ], 231 | "metadata": { 232 | "language_info": { 233 | "name": "python" 234 | } 235 | }, 236 | "nbformat": 4, 237 | "nbformat_minor": 1 238 | } 239 | -------------------------------------------------------------------------------- /test/test_ppxg_boost.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import pytest 5 | 6 | # This test file mainly tests binary prediction for xgboost 7 | # It tests all of the interfaces using OPE, Paillier, etc. 8 | import pickle as pl 9 | import pandas as pd 10 | import random 11 | 12 | import pyope.ope as pyope 13 | from ppxgboost import PaillierAPI as paillier 14 | import ppxgboost.OPEMetadata as OPEMetadata 15 | import ppxgboost.PPModel as PPModel 16 | import ppxgboost.PPTree as PPTree 17 | import ppxgboost.PPKey as PPKey 18 | import ppxgboost.PPQuery as PPQuery 19 | 20 | # The tests require modified input and output ranges 21 | in_range = pyope.ValueRange(pyope.DEFAULT_IN_RANGE_START, 2 ** 43 - 1) 22 | out_range = pyope.ValueRange(pyope.DEFAULT_OUT_RANGE_START, 2 ** 63 - 1) 23 | 24 | # Testing class for the pytest. To run simply "pytest test/" this will run all of the test in the test directory. 25 | class Test_PPMParser: 26 | 27 | # the testing for the parsing the model and the dumped trees. 28 | def test_model_parse(self): 29 | dir_path = "test_files/model_file.pkl" 30 | 31 | with open(dir_path, 'rb') as f: # will close() when we leave this block 32 | testing_model = pl.load(f) 33 | 34 | ppmodel = PPModel.from_xgboost_model(testing_model) 35 | 36 | # get the trees in string representation. 37 | # dump_tree is a list of strings, where each string is tree representation. See the docs in xgBoost for details. 38 | dump_tree = testing_model.get_dump() 39 | 40 | # for each one of trees, test if the parsed tree is the same as the tree object (calling print in tree object) 41 | for i in range(len(dump_tree)): 42 | assert dump_tree[i] == PPTree.tree_to_string(ppmodel.trees[i]) 43 | 44 | def test_ope_node(self): 45 | """ 46 | Test vector on evaluating the decision tree based on OPE 47 | :return: 48 | """ 49 | t1 = '0:[Pclass<3] yes=1,no=2,missing=1\n\t1:[Fare<13] yes=3,no=4,missing=3\n\t\t3:leaf=323\n\t\t4:[' \ 50 | 'Age<42] yes=9,no=10,missing=10\n\t\t\t9:leaf=32434\n\t\t\t10:leaf=43124\n\t2:[Age<6] yes=5,' \ 51 | 'no=6,missing=6\n\t\t5:[SibSp<32] yes=11,no=12,' \ 52 | 'missing=11\n\t\t\t11:leaf=9473\n\t\t\t12:leaf=836\n\t\t6:leaf=46\n ' 53 | 54 | input_vector = pd.read_csv('test_files/test_prediction_input.csv') 55 | 56 | ################################################################################################ 57 | # The following is to compute the scores for the decision tree on input vectors in plaintext! 58 | ################################################################################################ 59 | 60 | # As this test just to test the correctness of the encrypt_tree_node method 61 | ppModel = PPModel.PPModel([PPTree.parse_tree(t1)]) 62 | features = ppModel.get_features() 63 | 64 | # The score list value in plaintext. 65 | score_value = list() 66 | # get each row indexing with input vector's head 67 | queries = PPQuery.pandas_to_queries(input_vector) 68 | # Evaluating a model produces a list of results; we only want the result for the (one and) only tree in this model 69 | score_value = list(map(lambda q: ppModel.eval(q)[0], queries)) 70 | 71 | ################################################################################################ 72 | # The following is to compute the scores based on the OPE processed decision tree 73 | ################################################################################################ 74 | 75 | # Set up encrytion materials. 76 | ppModelKey, ppQueryKey = PPKey.generatePPXGBoostKeys(in_range, out_range) 77 | 78 | # as this only test the enc_tree_node ope, add fake metadata (min and max) for this computation 79 | # just for testing purposes. 80 | metadata = OPEMetadata.OPEMetadata(ppModel, 0, 1000, in_range.end) 81 | 82 | # 1. Encrypts the input vector for prediction (using prf_key_hash and ope-encrypter) based on the feature set. 83 | 84 | queryEncryptor = PPQuery.QueryEncryptor(ppQueryKey, features, metadata) 85 | enc_queries = PPQuery.encrypt_queries(queryEncryptor, queries) 86 | 87 | # 2. process the tree into ope_enc_tree 88 | enc_model = ppModel.encrypt(ppModelKey, metadata) 89 | 90 | # 3. OPE evaluation based on OPE encrypted values in the tree nodes. 91 | # Evaluating a model produces a list of results; we only want the result for the (one and) only tree in this model 92 | encrypted_value = list(map(lambda q: enc_model.eval(q)[0], enc_queries)) 93 | 94 | dec_value = list() 95 | for c in encrypted_value: 96 | dec_value.append(paillier.decrypt(ppQueryKey.get_private_key(), c)) 97 | 98 | # 4. compare 99 | assert dec_value == score_value 100 | 101 | def test_paillier_encryption(self): 102 | 103 | # randomly generate a number 104 | a = random.getrandbits(64) 105 | 106 | # Key Generation 107 | pub_key, priv_key = paillier.he_key_gen() 108 | # encrypt the random number 109 | enc_a = paillier.encrypt(pub_key, a) 110 | 111 | # test if decryption works. 112 | assert a == paillier.decrypt(priv_key, enc_a) 113 | 114 | def test_paillier_api(self): 115 | 116 | pub_key, priv_key = paillier.he_key_gen() 117 | 118 | # randomly generate the a and b from [0, sixty_four_bit_num) 119 | a = random.getrandbits(64) 120 | b = random.getrandbits(64) 121 | 122 | # encrypt a & b 123 | enc_a = paillier.encrypt(pub_key, a) 124 | enc_b = paillier.encrypt(pub_key, b) 125 | 126 | # homomorphically evaluate a + b 127 | # enc_a_b = enc(a+b) 128 | paillier.assert_ciphertext(enc_a) 129 | paillier.assert_ciphertext(enc_b) 130 | enc_a_b = enc_a + enc_b 131 | 132 | # try to catch the exception if the input is NOT a ciphertext 133 | try: 134 | paillier.assert_ciphertext(a) 135 | except ValueError: 136 | print("Input was not a ciphertext") 137 | assert True 138 | 139 | scalar = random.getrandbits(64) 140 | 141 | # encrypted_c_scalar = enc(scalar * a) 142 | encrypted_c_scalar = scalar * enc_a 143 | 144 | # perform all decryptions 145 | dec_a_b = paillier.decrypt(priv_key, enc_a_b) 146 | dec_c_scalar = paillier.decrypt(priv_key, encrypted_c_scalar) 147 | 148 | assert dec_a_b == a + b 149 | assert dec_c_scalar == scalar * a 150 | 151 | def test_paillier_negative_num_test(self): 152 | 153 | pub_key, priv_key = paillier.he_key_gen() 154 | 155 | # Negative number test # 156 | x = random.randint((-1) * random.getrandbits(64), 0) 157 | 158 | # encrypt negative number x 159 | enc_x = paillier.encrypt(pub_key, x) 160 | # encrypted negative number -x (a positive num) 161 | enc_pos_x = paillier.encrypt(pub_key, (-1) * x) 162 | 163 | paillier.assert_ciphertext(enc_pos_x) 164 | paillier.assert_ciphertext(enc_x) 165 | # homomorphic addition (enc_sum <- enc(0)) 166 | enc_sum = enc_pos_x + enc_x 167 | 168 | dec_x = paillier.decrypt(priv_key, enc_x) 169 | dec_sum = paillier.decrypt(priv_key, enc_sum) 170 | assert dec_x == x 171 | assert dec_sum == 0 172 | 173 | def test_serialization(self): 174 | 175 | pub_key, priv_key = paillier.he_key_gen() 176 | 177 | # randomly generate 64 bit number 178 | message = random.getrandbits(64) 179 | 180 | # Encrypt a message 181 | encrypted_message = paillier.encrypt(pub_key, message) 182 | # serialize it to json format 183 | json_str = paillier.ciphertext_serialization(pub_key, encrypted_message) 184 | 185 | # deserialize the json format 186 | pub_key, encrypted_number = paillier.ciphertext_deserialization(json_str) 187 | # decrypted the message using the correct private key. 188 | check_number = paillier.decrypt(priv_key, encrypted_number) 189 | 190 | assert message == check_number 191 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | Apache License 3 | Version 2.0, January 2004 4 | http://www.apache.org/licenses/ 5 | 6 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 7 | 8 | 1. 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All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import numpy as np 5 | import re 6 | 7 | from ppxgboost.PPKey import PPModelKey 8 | from ppxgboost.OPEMetadata import OPEMetadata 9 | import ppxgboost.PaillierAPI as paillier 10 | from ppxgboost.PPQuery import PPQuery, hmac_msg 11 | 12 | # This module implements a basic tree structure for XGBoost trees, 13 | # plus serialization to/deserialization from a string. The serialization 14 | # functionality allows us to import models from the xgboost library. 15 | 16 | class TreeNode: 17 | """ 18 | A parent class for interior and leaf nodes of a tree 19 | """ 20 | 21 | def __init__(self, identifier): 22 | """ 23 | :param identifier: a unique value identifying this node within the tree 24 | """ 25 | 26 | self.id = identifier 27 | 28 | 29 | class Leaf(TreeNode): 30 | """ 31 | A leaf node of a tree 32 | """ 33 | 34 | def __init__(self, identifier, value): 35 | """ 36 | :param identifier: a unique value identifying this node within the tree 37 | :param value: score for this leaf node 38 | """ 39 | 40 | super().__init__(identifier) 41 | self.value = value 42 | 43 | def eval(self, _): 44 | """ 45 | Evaluate the model on an input. Since this is a leaf node, 46 | we ignore the input and return the node's value. 47 | 48 | :return: result of evaluating the model on the provided query 49 | """ 50 | 51 | return self.value 52 | 53 | def node_to_string(self, lvl): 54 | """ 55 | Serialize this leaf as a string at level `lvl`. Note that this 56 | serialization is carefully designed to match the xgboost.get_dump() 57 | so that the deserialization function works for both model types. 58 | 59 | :param lvl: The level in the tree of this leaf 60 | :return: A string representing this leaf node 61 | """ 62 | 63 | return '\t'*lvl + str(self.id) + ":leaf=" + str(self.value) + "\n" 64 | 65 | def get_features(self): 66 | """ 67 | :return: The set of all features used in this XGBoost tree 68 | """ 69 | 70 | return set() 71 | 72 | def get_extreme_values(self): 73 | """ 74 | :return: the minimum and maximum values used for comparison in the tree 75 | """ 76 | 77 | return float('inf'), float('-inf') 78 | 79 | def encrypt(self, pp_boost_key: PPModelKey, metadata: OPEMetadata, feature_encryption_dict): 80 | """ 81 | Encrypt a plaintext XGBoost model 82 | 83 | :param pp_boost_key: The model encryption key 84 | :param metadata: OPE metadata 85 | :param feature_encryption_dict: dictionary from plaintext feature name to PRF'd feature name 86 | :return: a new PPModel corresponding to the encryption of `self` 87 | """ 88 | encrypted_value = paillier.encrypt(pp_boost_key.get_public_key(), self.value) 89 | return Leaf(self.id, encrypted_value) 90 | 91 | class Interior(TreeNode): 92 | """ 93 | An interior node in a (binary) tree 94 | """ 95 | 96 | def __init__(self, identifier, feature_name, cmp_val, if_true_child, if_false_child, default_child): 97 | """ 98 | :param identifier: a unique value identifying this node within the tree 99 | :param feature_name: the name of the feature to compare 100 | :param cmp_val: the value to compare for the feature. All comparisons are "x < cmp_val". 101 | :param if_true_child: the node to go to if the comparision statement is true. 102 | :param if_false_child: the node to go to if the comparision statement is false. 103 | :param default_child: the node to go to if the feature is missing from the data. This MUST be 104 | either if_true_child or if_false_child 105 | """ 106 | 107 | super().__init__(identifier) 108 | self.feature_name = feature_name 109 | self.cmp_val = cmp_val 110 | 111 | if (if_true_child != default_child and if_false_child != default_child): 112 | raise Exception("Default child must be either the 'true' child or the 'false' child") 113 | 114 | self.if_true_child = if_true_child 115 | self.if_false_child = if_false_child 116 | self.default_child = default_child 117 | 118 | def eval(self, x: PPQuery): 119 | """ 120 | Evaluate the model on the given query. 121 | 122 | :param x: dictionary corresponding to the input 123 | :return: result of evaluating the model on x 124 | """ 125 | 126 | if x is None: 127 | raise RuntimeError("None in eval") 128 | 129 | qd = x.query_dict 130 | if self.feature_name not in qd: 131 | print('Feature name ' + self.feature_name + ' is not available in query') 132 | print(qd) 133 | raise RuntimeError("Feature name not available in eval") 134 | 135 | if np.isnan(qd[self.feature_name]): 136 | return self.default_child.eval(x) 137 | 138 | if qd[self.feature_name] < self.cmp_val: 139 | return self.if_true_child.eval(x) 140 | else: 141 | return self.if_false_child.eval(x) 142 | 143 | def node_to_string(self, lvl): 144 | """ 145 | Serialize this tree as a string at level `lvl`. Note that this 146 | serialization is carefully designed to match the xgboost.get_dump() 147 | so that the deserialization function works for both model types. 148 | 149 | :param lvl: The level in the tree of this node 150 | :return: A string representing this tree node 151 | """ 152 | 153 | ans = "" 154 | for i in range(0, lvl): 155 | ans = ans + "\t" 156 | 157 | ans = "{}{}:[{}<{}] yes={},no={},missing={}\n".format(ans, str(self.id), str(self.feature_name), 158 | str(self.cmp_val), 159 | str(self.if_true_child.id), str(self.if_false_child.id), 160 | str(self.default_child.id)) 161 | 162 | return ans + self.if_true_child.node_to_string(lvl + 1) + self.if_false_child.node_to_string(lvl + 1) 163 | 164 | def get_features(self): 165 | """ 166 | :return: The set of features used by this model 167 | """ 168 | 169 | feature_set = set() 170 | feature_set.add(self.feature_name) 171 | feature_set = feature_set.union(self.if_true_child.get_features()) 172 | feature_set = feature_set.union(self.if_false_child.get_features()) 173 | return feature_set 174 | 175 | def get_extreme_values(self): 176 | """ 177 | :return: The minimum and maximum comparison values in the model 178 | """ 179 | 180 | min1, max1 = self.if_true_child.get_extreme_values() 181 | min2, max2 = self.if_false_child.get_extreme_values() 182 | return min(min1, min2, self.cmp_val), max(max1, max2, self.cmp_val) 183 | 184 | def encrypt(self, pp_boost_key: PPModelKey, metadata: OPEMetadata, feature_encryption_dict): 185 | """ 186 | Encrypt a plaintext XGBoost model 187 | 188 | :param pp_boost_key: The model encryption key 189 | :param metadata: OPE metadata 190 | :param feature_encryption_dict: dictionary from plaintext feature name to PRF'd feature name 191 | :return: a new PPModel corresponding to the encryption of `self` 192 | """ 193 | 194 | num = metadata.affine_transform(self.cmp_val) 195 | encrypted_val = pp_boost_key.get_ope_encryptor().encrypt(num) 196 | 197 | if self.feature_name not in feature_encryption_dict: 198 | feature_encryption_dict[self.feature_name] = hmac_msg(pp_boost_key.get_prf_key(), self.feature_name) 199 | encrypted_feature = feature_encryption_dict[self.feature_name] 200 | 201 | encrypted_true_subtree = self.if_true_child.encrypt(pp_boost_key, metadata, feature_encryption_dict) 202 | encrypted_false_subtree = self.if_false_child.encrypt(pp_boost_key, metadata, feature_encryption_dict) 203 | 204 | if self.if_true_child == self.default_child: 205 | encrypted_default_subtree = encrypted_true_subtree 206 | else: 207 | encrypted_default_subtree = encrypted_false_subtree 208 | 209 | return Interior(self.id, encrypted_feature, encrypted_val, encrypted_true_subtree, encrypted_false_subtree, encrypted_default_subtree) 210 | 211 | def tree_to_string(t: TreeNode): 212 | """ 213 | Serialize this tree as a string. Note that this 214 | serialization is carefully designed to match the xgboost.get_dump() 215 | so that the deserialization function works for both model types. 216 | 217 | :param t: the root of the tree 218 | :return: a string representing this tree node 219 | """ 220 | 221 | return t.node_to_string(0) 222 | 223 | 224 | # Create a PPXGBoost model from a string serialization. PPXGBoost requires 225 | # granular access to model parameters, but the xgboost library doesn't 226 | # provide this level of visibility directly. Instead, we designed 227 | # serialization of PPXGBoost models so that it matches the way that 228 | # xgboost serializes models via `get_dump()`. See 229 | # https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.get_dump 230 | # for details. Thus this function can parse 231 | # xgboost trees serialized via `get_dump()` _or_ PPXGBoost trees serialized 232 | # via `tree_to_string()`. 233 | def parse_subtree(s, lvl): 234 | """ 235 | Parse a subtree starting at a specific level. 236 | 237 | :param s: string representing the subtree 238 | :param lvl: level of this subtree in the full tree 239 | :return: a TreeNode representing this subtree 240 | """ 241 | 242 | # An example tree serialization string is: 243 | # 244 | # '0:[XXX<3] yes=1,no=2,missing=1\n\t1:[Fare<13.6458502] yes=3,no=4,missing=3\n\t\t 245 | # 3:leaf=-0.00585523667\n\t\t4:leaf=0.0201724116\n\t2:leaf=-0.0114313215\n 246 | # 247 | # This represents the following tree structure: 248 | # 0:[XXX<3] yes=1,no=2,missing=1 249 | # 1:[xyz<13.6458502] yes=3,no=4,missing=3 250 | # 3:leaf=-0.00585523667 251 | # 4:leaf=0.0201724116 252 | # 2:leaf=-0.0114313215 253 | 254 | # Recall that serialization (and therefore deserialization) are based on the 255 | # xgboost library's serialization approach. 256 | 257 | # split the string into chunks where each represents a single tree_node 258 | # the first item in the list is the root for this tree 259 | current_node = re.split(r'\n', s)[0] 260 | 261 | # a regular expression for parsing a variety of numeric formats 262 | # -?: an optional leading '-' sign 263 | # (\d*\.\d+|\d+): zero or more digits followed by a decimal and one or more digits 264 | # _or_ one or more digits (an integer). Order appears to matter here! 265 | # If the integer portion is first, a float that contains a whole number 266 | # component will match to it instead of the float clause. 267 | # (e-?\d+)?: optional scientific notation suffix. The exponent has an optional 268 | # '-' sign followed by one or more digits 269 | number_regex = r'-?(\d*\.\d+|\d+)(e-?\d+)?' 270 | 271 | # a regex for parsing feature names 272 | # This reads all characters lazily until the delimiter for the feature name '<' is reached 273 | feature_name_regex = r'[^<]+' 274 | 275 | # a regular expression for parsing a leaf node, which has the pattern ':leaf=' 276 | leaf_regex_str = r'(?P\d+):leaf=(?P' + number_regex + ')' 277 | 278 | leaf_node_regex = re.compile(leaf_regex_str) 279 | 280 | # a regex to parse an interior node, which has the pattern like '0:[XYZ ABC<3] yes=1,no=2,missing=1' 281 | interior_node_regex = re.compile(r'(?P\d+):\[(?P' + feature_name_regex + r')<(?P' + number_regex + r')] yes=(?P\d+),no=(?P\d+),missing=(?P\d+)') 282 | 283 | # convert a string to a numeric type 284 | # Unfortunately, we can't just use `float()` because 285 | # if the value is an integer in the string, it would 286 | # be serialized as a float (i.e., with a trailing '.0') 287 | # making the identity test fail 288 | def val_to_num(val): 289 | if val.isdigit(): 290 | return int(val) 291 | else: 292 | return float(val) 293 | 294 | # match current node against the two patterns 295 | leaf_regex_match = leaf_node_regex.match(current_node) 296 | if leaf_regex_match is not None: 297 | return Leaf(int(leaf_regex_match.group('id')), val_to_num(leaf_regex_match.group('val'))) 298 | 299 | interior_regex_match = interior_node_regex.match(current_node) 300 | if interior_regex_match is None: 301 | raise Exception("Invalid tree:\n" + current_node) 302 | 303 | # otherwise, we have successfully parsed an interior node 304 | 305 | 306 | # we've parsed the root, now find and parse the subtrees 307 | # subtrees are at level (lvl+1), which has a prefix with (lvl+1) tabs 308 | split_str = r'\n' + r'\t'*(lvl + 1) 309 | 310 | # Subtree matching extracts the left and right subtrees of current_node. 311 | # The '.+' matches current_node. 312 | # 'split_str' matches the subtree delimiter 313 | # '\d(.|\n)+' _ends_ the subtree delimiter (to avoid matching too many tabs for this level), 314 | # then matches one or more '.' (any char except \n) or \n 315 | # 'split_str' matches the second subtree delimiter 316 | # '\d(.|\n)+' _ends_ the subtree delimiter (to avoid matching too many tabs for this level), 317 | # then matches one or more '.' (any char except \n) or \n 318 | subtree_regex = re.compile(r'.+' + split_str + r'(?P\d(.|\n)+)' + split_str + r'(?P\d(.|\n)+)') 319 | subtree_match = subtree_regex.match(s) 320 | 321 | if subtree_match is None: 322 | raise Exception('invalid subtree structure\n' + repr(split_str) + '\n' + repr(s)) 323 | 324 | # recurse to the next level. 325 | left_subtree = parse_subtree(subtree_match.group('left_subtree'), lvl + 1) 326 | right_subtree = parse_subtree(subtree_match.group('right_subtree'), lvl + 1) 327 | 328 | # create a dictionary that maps the subtree Id to the subtree object 329 | child_dict = {left_subtree.id: left_subtree, right_subtree.id: right_subtree} 330 | 331 | return Interior(int(interior_regex_match.group('id')), 332 | interior_regex_match.group('feature'), 333 | val_to_num(interior_regex_match.group('cmp_val')), 334 | child_dict[int(interior_regex_match.group('true'))], 335 | child_dict[int(interior_regex_match.group('false'))], 336 | child_dict[int(interior_regex_match.group('default'))]) 337 | 338 | 339 | def parse_tree(tree_string): 340 | """ 341 | Parse a tree from its string serialization 342 | 343 | :param tree_string: string representation of the tree 344 | :return: a TreeNode for the root of the tree 345 | """ 346 | 347 | return parse_subtree(tree_string, 0) 348 | -------------------------------------------------------------------------------- /data/titanic_test.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. 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