{
    "info": {
        "author": "The TF Encrypted Authors",
        "author_email": "contact@tf-encrypted.io",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Security :: Cryptography"
        ],
        "description": "# TF Seal\n\nTF Seal provides a bridge between [TF Encrypted](https://github.com/tf-encrypted/tf-encrypted) and the [Microsoft SEAL](https://github.com/microsoft/SEAL) homomorphic encryption library, making it easier than ever to use this library to compute on encrypted data directly from TensorFlow.\n\n[![PyPI](https://img.shields.io/pypi/v/tf-seal.svg)](https://pypi.org/project/tf-seal/) [![CircleCI Badge](https://circleci.com/gh/tf-encrypted/tf-seal/tree/master.svg?style=svg)](https://circleci.com/gh/tf-encrypted/tf-seal/tree/master)\n\n## Usage\n\nThe following demonstrates how to perform a matrix multiplication using homomorphic encryption inside of TensorFlow.\n\n```\nimport numpy as np\nimport tensorflow as tf\n\nimport tf_seal as tfs\n\npublic_keys, secret_key = tfs.seal_key_gen(gen_relin=True, gen_galois=True)\n\n# encrypted input -> tf_seal.Tensor\na_plain = np.random.normal(size=(2, 2)).astype(np.float32)\na = tfs.constant(a_plain, secret_key, public_keys)\n\n# public weights\nb = np.random.normal(size=(2, 2)).astype(np.float32)\n\n# because of how the data is laid out in memory tfs.matmul expects\n# the b matrix to be order column-major wise\nc = tfs.matmul(a, b.transpose())\n\nwith tf.Session() as sess:\n    print(sess.run(c))\n```\n\n## Installation\n\nWe recommend using [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/distribution/) to set up and use a Python 3.7 environment for all instructions below:\n\n```\nconda create -n tfseal python=3.7 -y\nsource activate tfseal\n```\n\n### Custom TensorFlow\n\nA custom build of TensorFlow is currently needed to run TF Seal due to a mismatch between the C++ version used by the official TensorFlow build (C++11) and the one needed by Microsoft SEAL (C++17). A [patched version of TensorFlow](https://github.com/dropoutlabs/tensorflow) built with C++17 can be installed as shown below.\n\n#### Ubuntu\n\n```\nwget https://storage.googleapis.com/tf-pips/tf-c++17-support/tf_nightly-1.14.0-cp37-cp37m-linux_x86_64.whl\npip install tf_nightly-1.14.0-cp37-cp37m-linux_x86_64.whl\n```\n\n#### macOS\n\n```\nwget https://storage.googleapis.com/tf-pips/tf-c++17-support/tf_nightly-1.14.0-cp37-cp37m-macosx_10_7_x86_64.whl\npip install tf_nightly-1.14.0-cp37-cp37m-macosx_10_7_x86_64.whl\n```\n\nAfter installing the custom build of TensorFlow you can install TF Seal from [PyPi]() using pip:\n\n```\npip install tf-seal\n```\n\n## Examples\n\nThere is currently one example displaying how we can run a simple logistic regression prediction with TF SEAL.\n\nOnce TF SEAL is installed we can run the example by simplying running:\n\n```\npython logistic_regression.py\n```\n\n## Development\n\nWe recommend using [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/distribution/) to set up and use a Python 3.7 environment for all instructions below:\n\n```\nconda create -n tfseal-dev python=3.7 -y\nsource activate tfseal-dev\n```\n\n### Requirements\n\n#### Ubuntu\n\n- Python (== 3.7)\n- [Bazel](https://www.bazel.build/) (>= 0.26.1)\n- CMake\n- [TensorFlow built with C++17](#custom-tensorflow)\n\nCMake can be installed simply with apt:\n\n```\nsudo apt install cmake\n```\n\nBazel is a little more involved, the following instructions can be installed, recommend installing Bazel 0.26.1: https://docs.bazel.build/versions/master/install-ubuntu.html#install-with-installer-ubuntu\n\nThe remaining PyPI packages can then be installed using:\n\n```\npip install -r requirements-dev.txt\n```\n\nOnce the custom TensorFlow is installed you will be able to start development.\n\n#### macOS\n\nWe need the following items:\n\n- Python (== 3.7)\n- [Bazel](https://www.bazel.build/) (>= 0.26.1)\n- CMake\n- [TensorFlow built with C++17](#custom-tensorflow)\n\nUsing [Homebrew](https://brew.sh/) we make sure that both [Bazel](https://docs.bazel.build/versions/master/install-os-x.html#install-with-installer-mac-os-x) and CMake are installed:\n\n```\nbrew tap bazelbuild/tap\nbrew install bazelbuild/tap/bazel\nbrew install cmake\n```\n\nThe remaining PyPI packages can then be installed using:\n\n```\npip install -r requirements-dev.txt\n```\n\nOnce the custom TensorFlow is installed you will be able to start development.\n\n### Testing\n\nOnce the development environment is set up you can run:\n\n```\nmake test\n```\n\n\n\n",
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