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              • numth.py
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        • __init__.py
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          • hook.py
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        • __init__.py
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      • autoscale
        • test.ipynb
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        • credential.py
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      • state.py
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      • translation
        • default.py
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    • federated
      • fl_client.py
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      • __init__.py
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    • messaging
      • message.py
      • __init__.py
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      • serde.py
      • protobuf
        • serde.py
        • native_serde.py
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      • msgpack
        • serde.py
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        • __init__.py
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        • serde.py
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  • images
  • examples
    • tutorials
      • Part 13c - Secure Classification with Syft Keras and TFE - Private Prediction Client.ipynb
      • Part 13a - Secure Classification with Syft Keras and TFE - Public Training.ipynb
      • Part 12 bis - Encrypted Training on MNIST.ipynb
      • translations
        • romanian
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
        • german
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 03 - Advanced Remote Execution Tools.ipynb
          • Part 08 bis - Introduction to Protocols.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
          • Part 07 - Federated Learning with Federated Dataset.ipynb
          • Part 08 - Introduction to Plans.ipynb
          • Part 10 - Federated Learning with Secure Aggregation.ipynb
          • Part 06 - Federated Learning on MNIST using a CNN.ipynb
          • Part 05 - Welcome to the Sandbox.ipynb
          • Part 09 - Intro to Encrypted Programs.ipynb
        • korean
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
        • español
          • Parte 03 - Herramientas avanzadas de ejecución remota.ipynb
          • Parte 04 - Aprendizaje Federado via un Agregador Confiable.ipynb
          • Parte 10 - Aprendizaje Federado con Agregación Segura.ipynb
          • Parte 12 - Entrenar una red neuronal encriptada con datos encriptados .ipynb
          • Parte 12 bis - Entrenamiento Encriptado con MNIST.ipynb
          • Parte 01 - Herramientas básicas de aprendizaje profundo privado.ipynb
          • Parte 11 - Clasificación con Aprendizaje Profundo y Seguro.ipynb
          • Parte 08 bis - Introducción a Protocolos.ipynb
          • Parte 05 - Bienvenido a la caja de arena.ipynb
          • Parte 02 - Intro al Aprendizaje Federado.ipynb
          • Parte 06 - Aprendizaje federado en MNIST usando una red neuronal convolucional.ipynb
          • Parte 13a - Clasificación Segura con Syft Keras y TFE - Aprendizaje Público.ipynb
          • Parte 13c - Clasificación Segura con Syft Keras y TFE - Prediccion Privada (Cliente).ipynb
          • Parte 07 - Aprendizaje Federado con Datos Federados.ipynb
          • Parte 13b - Clasificación Segura con Syft Keras y TFE - Proveer Predicciones desde un Modelo Seguro.ipynb
          • Parte 09 - Intro a los Programas Encriptados.ipynb
          • Parte 08 - Introducción a los Planes.ipynb
        • hindi
          • Part 13c - Secure Classification with Syft Keras and TFE - Private Prediction Client.ipynb
          • Part 13a - Secure Classification with Syft Keras and TFE - Public Training.ipynb
          • Part 12 bis - Encrypted Training on MNIST.ipynb
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 03 - Advanced Remote Execution Tools.ipynb
          • Part 08 bis - Introduction to Protocols.ipynb
          • Part 11 - Secure Deep Learning Classification.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
          • Part 07 - Federated Learning with Federated Dataset.ipynb
          • Part 08 - Introduction to Plans.ipynb
          • Part 10 - Federated Learning with Secure Aggregation.ipynb
          • Part 06 - Federated Learning on MNIST using a CNN.ipynb
          • Part 13b - Secure Classification with Syft Keras and TFE - Secure Model Serving.ipynb
          • Part 05 - Welcome to the Sandbox.ipynb
          • Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb
          • Part 09 - Intro to Encrypted Programs.ipynb
        • Pidgin
          • Part 05 - Welicome to Sandbox-Pidgin.ipynb
          • Part 03 - Advanced Remote Execution Tools-Pidgin.ipynb
          • Part 07 - Make we do Federated Learning with FederatedDataset-Pidgin.ipynb
          • Part 02 - Intro to Federated Learning- Pidgin.ipynb
          • Part 01 - Basic Tools wey you need for Private Deep Learning.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator-Pidgin.ipynb
          • README.md
          • Part 09 - Intro to Encrypted Programs-Pidgin.ipynb
        • marathi
          • Part 13c - Secure Classification with Syft Keras and TFE - Private Prediction Client.ipynb
          • Part 13a - Secure Classification with Syft Keras and TFE - Public Training.ipynb
          • Part 12 bis - Encrypted Training on MNIST.ipynb
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 03 - Advanced Remote Execution Tools.ipynb
          • Part 08 bis - Introduction to Protocols.ipynb
          • Part 11 - Secure Deep Learning Classification.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
          • Part 07 - Federated Learning with Federated Dataset.ipynb
          • Part 08 - Introduction to Plans.ipynb
          • Part 10 - Federated Learning with Secure Aggregation.ipynb
          • Part 06 - Federated Learning on MNIST using a CNN.ipynb
          • Part 13b - Secure Classification with Syft Keras and TFE - Secure Model Serving.ipynb
          • Part 05 - Welcome to the Sandbox.ipynb
          • Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb
          • Part 09 - Intro to Encrypted Programs.ipynb
        • italian
          • Parte 01 - Gli strumenti di base per il deep learning privato.ipynb
        • português
          • Parte 10 - Aprendizagem Federada com Agregação Segura.ipynb
          • Parte 09 - Introdução à Programas Criptografados.ipynb
          • Parte 04 - Aprendizado Federado por meio de Agregador Confiável.ipynb
          • Parte 08 bis - Introdução a Protocolos.ipynb
          • Parte 13c - Classificação Segura com Syft Keras e TFE - Cliente de predição privada.ipynb
          • Parte 12 bis - Treinamento criptografado no MNIST.ipynb
          • Parte 13a - Classificação Segura com Syft Keras e TFE - Treinamento Público.ipynb
          • Parte 06 - Aprendizado Federado com MNIST usando uma CNN.ipynb
          • Parte 05 - Bem-vindo ao Sandbox.ipynb
          • Parte 08 - Introdução a Planos.ipynb
          • Parte 07 - Aprendizado Federado com Conjunto de Dados Federado.ipynb
          • Parte 12 - Treinar uma Rede Neural criptografada com dados criptografados.ipynb
          • Parte 11 - Classificação Segura Usando Aprendizagem Profunda.ipynb
          • Parte 02 - Introdução a Aprendizagem Federada.ipynb
          • Parte 03 - Ferramentas Avançadas para Execução Remota.ipynb
          • Parte 13b - Classificação segura com Syft Keras e TFE - Servindo o modelo de forma segura.ipynb
          • Parte 01 - As Ferramentas Básicas do Private Deep Learning.ipynb
        • chinese
          • Part 13c - Secure Classification with Syft Keras and TFE - Private Prediction Client.ipynb
          • Part 13a - Secure Classification with Syft Keras and TFE - Public Training.ipynb
          • Part 12 bis - Encrypted Training on MNIST.ipynb
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 03 - Advanced Remote Execution Tools.ipynb
          • Part 08 bis - Introduction to Protocols.ipynb
          • Part 11 - Secure Deep Learning Classification.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
          • Part 07 - Federated Learning with Federated Dataset.ipynb
          • Part 08 - Introduction to Plans.ipynb
          • Part 10 - Federated Learning with Secure Aggregation.ipynb
          • Part 06 - Federated Learning on MNIST using a CNN.ipynb
          • Part 13b - Secure Classification with Syft Keras and TFE - Secure Model Serving.ipynb
          • Part 05 - Welcome to the Sandbox.ipynb
          • Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb
          • Part 09 - Intro to Encrypted Programs.ipynb
        • indonesian
          • Bagian 01 - Peralatan Dasar untuk Privasi Pembelajaran Dalam.ipynb
        • japanese
          • Part 13c - Secure Classification with Syft Keras and TFE - Private Prediction Client.ipynb
          • Part 13a - Secure Classification with Syft Keras and TFE - Public Training.ipynb
          • Part 12 bis - Encrypted Training on MNIST.ipynb
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 03 - Advanced Remote Execution Tools.ipynb
          • Part 08 bis - Introduction to Protocols.ipynb
          • Part 11 - Secure Deep Learning Classification.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
          • Part 07 - Federated Learning with Federated Dataset.ipynb
          • Part 08 - Introduction to Plans.ipynb
          • Part 10 - Federated Learning with Secure Aggregation.ipynb
          • Part 06 - Federated Learning on MNIST using a CNN.ipynb
          • Part 13b - Secure Classification with Syft Keras and TFE - Secure Model Serving.ipynb
          • Part 05 - Welcome to the Sandbox.ipynb
          • Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb
          • Part 09 - Intro to Encrypted Programs.ipynb
        • french
          • Part_01_The_Basic_Tools_of_Private_Deep_Learning.ipynb
        • bengali
          • Part 13c - Secure Classification with Syft Keras and TFE - Private Prediction Client.ipynb
          • Part 13a - Secure Classification with Syft Keras and TFE - Public Training.ipynb
          • Part 12 bis - Encrypted Training on MNIST.ipynb
          • Part 02 - Intro to Federated Learning.ipynb
          • Part 03 - Advanced Remote Execution Tools.ipynb
          • Part 11 - Secure Deep Learning Classification.ipynb
          • Part 04 - Federated Learning via Trusted Aggregator.ipynb
          • Part 01 - The Basic Tools of Private Deep Learning.ipynb
          • Part 07 - Federated Learning with Federated Dataset.ipynb
          • Part 08 - Introduction to Plans.ipynb
          • Part 10 - Federated Learning with Secure Aggregation.ipynb
          • Part 06 - Federated Learning on MNIST using a CNN.ipynb
          • Part 13b - Secure Classification with Syft Keras and TFE - Secure Model Serving.ipynb
          • Part 05 - Welcome to the Sandbox.ipynb
          • Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb
          • Part 09 - Intro to Encrypted Programs.ipynb
        • ukrainian
          • Part 01 - Основні засоби приватного глибинного навчання.ipynb
      • Part 02 - Intro to Federated Learning.ipynb
      • Part 03 - Advanced Remote Execution Tools.ipynb
      • websocket
        • WebsocketServerWorker.ipynb
        • pen_testing
          • __init__.py
          • steal_data_over_sockets
            • start_websocket_servers.py
            • __init__.py
            • README.md
            • Challenge Setup.ipynb
        • __init__.py
        • WebsocketClientWorker.ipynb
        • deploy_workers
          • docker-compose.yaml
          • deploy-and-connect.ipynb
          • __init__.py
          • README.md
      • Part 11 - Secure Deep Learning Classification.ipynb
      • Part 04 - Federated Learning via Trusted Aggregator.ipynb
      • Part 01 - The Basic Tools of Private Deep Learning.ipynb
      • grid
        • Part 01 - Intro to Grid Platform.ipynb
        • Part 02 - Grid as a Secure MLaaS to Cloud Providers.ipynb
        • images
        • __init__.py
        • federated_learning
          • spam_prediction
            • preprocess.py
            • handcrafted_GRU.py
            • Federated_SMS_Spam_prediction [Part 2] - Train a Model.ipynb
            • __init__.py
            • Federated_SMS_Spam_prediction [Part 1] - Populate a Grid Network (Dataset).ipynb
          • __init__.py
          • mnist
            • Fed.Learning MNIST [ Part-2 ] - Train a Model.ipynb
            • __init__.py
            • Fed.Learning MNIST [ Part-1 ] - Populate a Grid Network ( Dataset ).ipynb
        • Part 03 - Grid applied to Smart Cities and Smart Homes.ipynb
      • Part 07 - Federated Learning with Federated Dataset.ipynb
      • material
      • Part 08 - Introduction to Plans.ipynb
      • __init__.py
      • Part 10 - Federated Learning with Secure Aggregation.ipynb
      • README.md
      • advanced
        • Federated Word Vectors.ipynb
        • Federated CIFAR10.ipynb
        • Federated Recurrent Neural Network.ipynb
        • privacy_attacks
          • Tutorial 1 - Black box model inversion.ipynb
        • monitor_network_traffic
          • metrics.ipynb
          • __init__.py
        • split_neural_network
          • images
          • Tutorial 1 - SplitNN Introduction.ipynb
          • Tutorial 3 - Folded Split Neural Network.ipynb
          • Tutorial 2 - MultiLayer Split Neural Network.ipynb
          • __init__.py
        • websockets_mnist
          • start_websocket_servers.py
          • run_websocket_client.py
          • __init__.py
          • README.md
          • Federated learning with websockets and federated averaging.ipynb
        • __init__.py
        • Encrypted Linear Regression.ipynb
        • Build your own tensor type (advanced).ipynb
        • federated_sms_spam_prediction
          • preprocess.py
          • handcrafted_GRU.py
          • __init__.py
          • Federated SMS Spam prediction.ipynb
        • Federated Dataset.ipynb
      • Part 06 - Federated Learning on MNIST using a CNN.ipynb
      • Part 13b - Secure Classification with Syft Keras and TFE - Secure Model Serving.ipynb
      • Part 05 - Welcome to the Sandbox.ipynb
      • Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb
      • Part 09 - Intro to Encrypted Programs.ipynb
    • data
      • BostonHousing
        • boston_housing.pickle
    • __init__.py
    • experimental
      • NumpyTensor.ipynb
      • Private Tensor Mockup.ipynb
      • plaintext_speed_regression.ipynb
      • Large Precision Tensor.ipynb
      • PromiseTensor Mockup.ipynb
      • PaillierTensor.ipynb
      • PointersAhead.ipynb
      • DP Tensor Experimentation - v2.ipynb
      • DP Tensor Experimentation - v5.ipynb
      • DP Tensor Experimentation - v1.ipynb
      • Federated Learning Reinforcement Learning Agent.ipynb
      • README.md
      • DP Tensor Experimentation - v8.ipynb
      • FL Training Plan
        • Create Plan.ipynb
        • Execute Plan with Python FL Client.ipynb
        • Execute Plan.ipynb
        • Host Plan.ipynb
      • Federated Learning Experiment
        • server.py
        • FL Experiment.ipynb
      • PATE Analysis.ipynb
      • DP Tensor Experimentation - v4.ipynb
      • DP Tensor Experimentation - v3.ipynb
      • FV HE Experimentation.ipynb
    • .gitignore
  • .pre-commit-config.yaml
  • docker-images
    • pysyft-notebook
      • entrypoint.sh
      • Dockerfile
    • pysyft-worker
      • entrypoint.sh
      • worker-server.py
      • Dockerfile
  • codecov.yml
  • LICENSE
  • test
    • efficiency
      • test_linalg_time.py
      • test_activations_time.py
      • assertions.py
      • __init__.py
    • test_grid.py
    • test_exceptions.py
    • common
      • test_util.py
    • test_local_worker.py
    • workers
      • test_base.py
      • test_worker.py
      • __init__.py
      • test_virtual.py
      • test_websocket_worker.py
    • test_dependency_check.py
    • message
      • test_message.py
      • __init__.py
    • generic
      • test_autograd.py
      • test_object_storage.py
      • test_private.py
      • test_gc.py
      • test_string.py
      • frameworks
        • test_attributes.py
      • test_hookable.py
      • test_functions.py
      • __init__.py
      • test_id_provider.py
      • pointers
        • test_dataset_pointer.py
        • test_multi_pointer.py
        • test_pointer_plan.py
        • __init__.py
        • test_callable_pointer.py
        • test_pointer_tensor.py
      • test_logging.py
    • test_sandbox.py
    • notebooks
      • test_notebooks.py
      • git-diff.txt
    • test_udacity.py
    • torch
      • test_federated_learning.py
      • hook
        • test_hook_args.py
        • __init__.py
        • test_hook.py
      • mpc
        • test_multiparty_nn.py
        • test_crypto_store.py
        • test_fss.py
        • test_securenn.py
        • __init__.py
      • tensors
        • test_additive_shared.py
        • test_polynomial.py
        • test_tensor.py
        • test_native.py
        • test_precision.py
        • test_paillier.py
        • test_parameter.py
        • test_fv.py
        • test_numpy.py
        • __init__.py
      • nn
        • test_functional.py
        • __init__.py
        • test_nn.py
      • differential_privacy
        • test_pate.py
        • __init__.py
      • federated
        • test_utils.py
        • test_dataset.py
        • test_dataloader.py
        • __init__.py
      • __init__.py
      • linalg
        • test_operations.py
        • test_lr.py
        • __init__.py
      • test_hook.py
    • execution
      • test_state.py
      • test_plan.py
      • test_protocol.py
      • test_translation.py
      • test_role_assignments.py
      • test_role.py
      • test_placeholder.py
      • test_package_wrapper.py
      • test_communication.py
    • __init__.py
    • scripts
      • run_websocket_server.py
    • run_websocket_server.py
    • keras
      • test_sequential.py
    • serde
      • serde_helpers.py
      • protobuf
        • test_protobuf_serde_full.py
        • test_protobuf_serde.py
      • msgpack
        • test_msgpack_serde.py
        • test_msgpack_serde_full.py
    • conftest.py
  • .readthedocs.yml
  • CONTRIBUTING.md
  • .flake8
  • .gitattributes
  • mypy.ini
  • ROADMAP.md
  • setup.py
  • setup.cfg
  • README.md
  • pyproject.toml
  • run_websocket_server.py
  • .gitignore
  • docs
    • Makefile
    • source
      • index.rst
      • conf.py
    • make.bat
  • MANIFEST.in

from math import gcd

from syft.frameworks.torch.he.fv.util.operations import multiply_many_except
from syft.frameworks.torch.he.fv.util.operations import invert_mod


class RNSBase:
    """A model class for creating basic blocks required in RNSTools class with
    pre-computed attributes.

    Args:
        base: A list of Base values.

    Attributes:
        size: The number of base values given.
        base: A list of Base values.
        base_prod: An integer denoting the product of all base values.

        punctured_prod_list: A list of products of all base values except
        the base value at that index.

        inv_punctured_prod_mod_base_list: A list of values equal to modulus
        inverse of punctured_prod_list values.
    """

    def __init__(self, base):
        self.size = len(base)

        for i in range(self.size):
            if base[i] == 0:
                raise ValueError("rns_base is invalid")

            # The base must be coprime
            for j in base[:i]:
                if gcd(base[i], j) != 1:
                    raise ValueError("rns_base is invalid")

        self.base = base
        self.base_prod = None
        self.punctured_prod_list = [0] * self.size
        self.inv_punctured_prod_mod_base_list = [0] * self.size

        if self.size > 1:
            # Compute punctured product
            for i in range(self.size):
                self.punctured_prod_list[i] = multiply_many_except(self.base, self.size, i)

            # Compute the full product
            self.base_prod = self.punctured_prod_list[0] * self.base[0]

            # Compute inverses of punctured products mod primes
            for i in range(self.size):
                self.inv_punctured_prod_mod_base_list[i] = (
                    self.punctured_prod_list[i] % self.base[i]
                )
                self.inv_punctured_prod_mod_base_list[i] = invert_mod(
                    self.inv_punctured_prod_mod_base_list[i], self.base[i]
                )

        else:
            self.base_prod = self.base[0]
            self.punctured_prod_list[0] = 1
            self.inv_punctured_prod_mod_base_list[0] = 1