Health_Neural_Network.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Health Neural Network",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RocketRikky/Secure_and_Private_AI/blob/master/Health_Neural_Network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPCxyOMoXMm7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yTa02PcYpyW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_teachers = 10 #working with 10 hospitals\n",
        "num_examples = 10000 \n",
        "num_labels = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xal8BamyYuo2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = (np.random.rand(num_teachers, num_examples) * num_labels).astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu5i7hG5Y-YX",
        "colab_type": "code",
        "outputId": "815faea8-1320-4bb3-daed-4d3bb1f1fe7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "preds"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3, 6, 8, ..., 0, 7, 8],\n",
              "       [0, 3, 6, ..., 8, 2, 2],\n",
              "       [6, 8, 6, ..., 4, 8, 7],\n",
              "       ...,\n",
              "       [8, 1, 9, ..., 6, 7, 8],\n",
              "       [5, 0, 8, ..., 3, 4, 2],\n",
              "       [4, 8, 8, ..., 4, 9, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyYuKXC_Y-3_",
        "colab_type": "code",
        "outputId": "3c93524c-e834-4002-8670-9653c62d2a14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preds[0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlDWE_-MZIM4",
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      },
      "source": [
        "preds[:,0]\n",
        "# this is telling that the first element is predicting label 3, second element is predicting label 0 and so on"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 0, 6, 3, 2, 6, 2, 8, 5, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
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      },
      "source": [
        "an_image = preds[:,0]\n",
        "an_image"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 0, 6, 3, 2, 6, 2, 8, 5, 4])"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "leilNwGraVwy",
        "colab_type": "code",
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      "source": [
        "label_counts = np.bincount(an_image, minlength=num_labels)\n",
        "# in an_image, 0 appeared 1 time and 2 appeared 2 times and so on."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q98csujUbAkI",
        "colab_type": "code",
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      "source": [
        "epsilon = 0.1\n",
        "beta = 1 / epsilon\n",
        "\n",
        "for i in range(len(label_counts)):\n",
        "  label_counts[i] += np.random.laplace(0, beta, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s84l0rLebSzG",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "label_counts"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  5,   0, -20,   0, -14,   0,   8, -11,  -4, -14])"
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          "execution_count": 16
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        "id": "v1DjcEMnaiWE",
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      "source": [
        "new_label = np.argmax(label_counts)"
      ],
      "execution_count": 0,
      "outputs": []
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    {
      "cell_type": "code",
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        "id": "WPWh0cG4azXC",
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUJk25Iqcu6G",
        "colab_type": "code",
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      },
      "source": [
        "preds = (np.random.rand(num_teachers, num_examples) * num_labels).astype(int).transpose(1,0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_j-ZoqlJcGnC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_labels = list()\n",
        "for an_image in preds:\n",
        "  \n",
        "  label_counts = np.bincount(an_image, minlength=num_labels)\n",
        "  \n",
        "  epsilon = 0.1\n",
        "  beta = 1 / epsilon\n",
        "  \n",
        "  for i in range(len(label_counts)):\n",
        "    label_counts[i] += np.random.laplace(0, beta, 1)\n",
        "    \n",
        "  new_label = np.argmax(label_counts)\n",
        "  \n",
        "  new_labels.append(new_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePwjWj11cnxC",
        "colab_type": "code",
        "outputId": "360cd6d3-3d50-4ed7-af3e-441972a5a861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(new_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
        "outputId": "d467453d-0d1d-4bb7-a480-58d2c255600a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "new_labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdUkugA5gd11",
        "colab_type": "text"
      },
      "source": [
        "PATE Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxkpolZbeBrQ",
        "colab_type": "code",
        "outputId": "57023e2e-d0cb-427b-c836-89c2f947829f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "labels = np.array([9, 9, 3, 6, 9, 9, 9, 9, 8, 2])\n",
        "counts = np.bincount(labels, minlength=10)\n",
        "query_result = np.argmax(counts)\n",
        "query_result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WR2iNG1SzLaQ",
        "colab_type": "code",
        "outputId": "8dc8c100-160d-4580-8a1c-1adb2896414f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install syft"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting syft\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5b/25/633ddb891b3c4927bd03311a04ece038387faecb46120b8429ed28c72c13/syft-0.1.23a1-py3-none-any.whl (251kB)\n",
            "\u001b[K     |████████████████████████████████| 256kB 4.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from syft) (1.16.4)\n",
            "Collecting websocket-client>=0.56.0 (from syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/29/19/44753eab1fdb50770ac69605527e8859468f3c0fd7dc5a76dd9c4dbd7906/websocket_client-0.56.0-py2.py3-none-any.whl (200kB)\n",
            "\u001b[K     |████████████████████████████████| 204kB 44.9MB/s \n",
            "\u001b[?25hCollecting tf-encrypted!=0.5.7,>=0.5.4 (from syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1f/82/cf15aeac92525da2f794956712e7ebf418819390dec783430ee242b52d0b/tf_encrypted-0.5.8-py3-none-manylinux1_x86_64.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 44.9MB/s \n",
            "\u001b[?25hCollecting msgpack>=0.6.1 (from syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/92/7e/ae9e91c1bb8d846efafd1f353476e3fd7309778b582d2fb4cea4cc15b9a2/msgpack-0.6.1-cp36-cp36m-manylinux1_x86_64.whl (248kB)\n",
            "\u001b[K     |████████████████████████████████| 256kB 48.6MB/s \n",
            "\u001b[?25hCollecting flask-socketio>=3.3.2 (from syft)\n",
            "  Downloading https://files.pythonhosted.org/packages/66/44/edc4715af85671b943c18ac8345d0207972284a0cd630126ff5251faa08b/Flask_SocketIO-4.2.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: torch==1.1 in /usr/local/lib/python3.6/dist-packages (from syft) (1.1.0)\n",
            "Requirement already satisfied: torchvision==0.3.0 in /usr/local/lib/python3.6/dist-packages (from syft) (0.3.0)\n",
            "Requirement already satisfied: scikit-learn>=0.21.0 in /usr/local/lib/python3.6/dist-packages (from syft) (0.21.3)\n",
            "Collecting zstd>=1.4.0.0 (from syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/37/6a7ba746ebddbd6cd06de84367515d6bc239acd94fb3e0b1c85788176ca2/zstd-1.4.1.0.tar.gz (454kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 40.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: tblib>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from syft) (1.4.0)\n",
            "Collecting lz4>=2.1.6 (from syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0a/c6/96bbb3525a63ebc53ea700cc7d37ab9045542d33b4d262d0f0408ad9bbf2/lz4-2.1.10-cp36-cp36m-manylinux1_x86_64.whl (385kB)\n",
            "\u001b[K     |████████████████████████████████| 389kB 38.7MB/s \n",
            "\u001b[?25hCollecting websockets>=7.0 (from syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f0/4b/ad228451b1c071c5c52616b7d4298ebcfcac5ae8515ede959db19e4cd56d/websockets-8.0.2-cp36-cp36m-manylinux1_x86_64.whl (72kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 23.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: Flask>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from syft) (1.1.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from websocket-client>=0.56.0->syft) (1.12.0)\n",
            "Collecting pyyaml>=5.1 (from tf-encrypted!=0.5.7,>=0.5.4->syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e3/e8/b3212641ee2718d556df0f23f78de8303f068fe29cdaa7a91018849582fe/PyYAML-5.1.2.tar.gz (265kB)\n",
            "\u001b[K     |████████████████████████████████| 266kB 46.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow<2,>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tf-encrypted!=0.5.7,>=0.5.4->syft) (1.14.0)\n",
            "Collecting python-socketio>=4.3.0 (from flask-socketio>=3.3.2->syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/35/b0/22c3f785f23fec5c7a815f47c55d7e7946a67ae2129ff604148e939d3bdb/python_socketio-4.3.1-py2.py3-none-any.whl (49kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 18.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.3.0->syft) (4.3.0)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.21.0->syft) (1.3.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.21.0->syft) (0.13.2)\n",
            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->syft) (0.15.5)\n",
            "Requirement already satisfied: Jinja2>=2.10.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->syft) (2.10.1)\n",
            "Requirement already satisfied: itsdangerous>=0.24 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->syft) (1.1.0)\n",
            "Requirement already satisfied: click>=5.1 in /usr/local/lib/python3.6/dist-packages (from Flask>=1.0.2->syft) (7.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (0.2.2)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (1.0.8)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (1.11.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (0.33.4)\n",
            "Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (1.14.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (1.15.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (1.1.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (1.1.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (0.7.1)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (3.7.1)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (0.1.7)\n",
            "Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (1.14.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (0.8.0)\n",
            "Collecting python-engineio>=3.9.0 (from python-socketio>=4.3.0->flask-socketio>=3.3.2->syft)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2b/20/8e3ba16102ae2e245d70d9cb9fa48b076253fdb036dc43eea142294c2897/python_engineio-3.9.3-py2.py3-none-any.whl (119kB)\n",
            "\u001b[K     |████████████████████████████████| 122kB 39.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision==0.3.0->syft) (0.46)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.10.1->Flask>=1.0.2->syft) (1.1.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (2.8.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (41.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow<2,>=1.12.0->tf-encrypted!=0.5.7,>=0.5.4->syft) (3.1.1)\n",
            "Building wheels for collected packages: zstd, pyyaml\n",
            "  Building wheel for zstd (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for zstd: filename=zstd-1.4.1.0-cp36-cp36m-linux_x86_64.whl size=1067080 sha256=dd8203dc5980ffb81a2785f866c77b6bf1af804ce1f2d9fd23e89469e6f66391\n",
            "  Stored in directory: /root/.cache/pip/wheels/66/3f/ee/ac08c81af7c1b24a80c746df669ea3cb37542d27877d66ccf4\n",
            "  Building wheel for pyyaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyyaml: filename=PyYAML-5.1.2-cp36-cp36m-linux_x86_64.whl size=44105 sha256=2825773d365772ed9d612e05ad4754b1abeb58834400bf53f639325f0f5b27f0\n",
            "  Stored in directory: /root/.cache/pip/wheels/d9/45/dd/65f0b38450c47cf7e5312883deb97d065e030c5cca0a365030\n",
            "Successfully built zstd pyyaml\n",
            "Installing collected packages: websocket-client, pyyaml, tf-encrypted, msgpack, python-engineio, python-socketio, flask-socketio, zstd, lz4, websockets, syft\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Found existing installation: msgpack 0.5.6\n",
            "    Uninstalling msgpack-0.5.6:\n",
            "      Successfully uninstalled msgpack-0.5.6\n",
            "Successfully installed flask-socketio-4.2.1 lz4-2.1.10 msgpack-0.6.1 python-engineio-3.9.3 python-socketio-4.3.1 pyyaml-5.1.2 syft-0.1.23a1 tf-encrypted-0.5.8 websocket-client-0.56.0 websockets-8.0.2 zstd-1.4.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2uFkEmy08l7",
        "colab_type": "code",
        "outputId": "94c895f1-dcbb-4f88-ae9c-2f107e6938e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "from syft.frameworks.torch.differential_privacy import pate"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0817 09:21:30.972918 139835399337856 secure_random.py:26] Falling back to insecure randomness since the required custom op could not be found for the installed version of TensorFlow. Fix this by compiling custom ops. Missing file was '/usr/local/lib/python3.6/dist-packages/tf_encrypted/operations/secure_random/secure_random_module_tf_1.14.0.so'\n",
            "W0817 09:21:30.990077 139835399337856 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tf_encrypted/session.py:26: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROcl5L_L4ovF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_teachers, num_examples, num_labels = (100, 100, 10)\n",
        "preds = (np.random.rand(num_teachers, num_examples) * num_labels).astype(int) #fake preds\n",
        "true_indices = (np.random.rand(num_examples) * num_labels).astype(int) #true answers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRm44idD4biW",
        "colab_type": "code",
        "outputId": "49689c3c-1b2d-4bc4-e17c-64137c0220a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "data_dep_eps, data_inde_eps = pate.perform_analysis(teacher_preds=preds, indices=true_indices, noise_eps=0.1, delta=1e-5)\n",
        "print(\"Data Independent Epsilon: \", data_inde_eps)\n",
        "print(\"Data dependent Epsilon: \", data_dep_eps)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data Independent Epsilon:  11.756462732485115\n",
            "Data dependent Epsilon:  11.756462732485105\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hmO9ErH5g_t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds[:,0:5] *= 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ND5JXIbX6W5d",
        "colab_type": "code",
        "outputId": "d82f0a07-d2d3-4cd9-e396-6b1c20c80627",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "data_dep_eps, data_inde_eps = pate.perform_analysis(teacher_preds=preds, indices=true_indices, noise_eps=0.1, delta=1e-5)\n",
        "print(\"Data Independent Epsilon: \", data_inde_eps)\n",
        "print(\"Data dependent Epsilon: \", data_dep_eps)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data Independent Epsilon:  11.756462732485115\n",
            "Data dependent Epsilon:  7.612485787172064\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWx5tVg_6UUY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds[:,0:50] *= 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkxhADVK6dGm",
        "colab_type": "code",
        "outputId": "1b7f20ac-de04-41ed-8ed7-2ae0bdfea0f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "data_dep_eps, data_inde_eps = pate.perform_analysis(teacher_preds=preds, indices=true_indices, noise_eps=0.1, delta=1e-5)\n",
        "print(\"Data Independent Epsilon: \", data_inde_eps)\n",
        "print(\"Data dependent Epsilon: \", data_dep_eps)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: May not have used enough values of l. Increase 'moments' variable and run again.\n",
            "Data Independent Epsilon:  11.756462732485115\n",
            "Data dependent Epsilon:  1.52655213289881\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpHJruna7HYp",
        "colab_type": "text"
      },
      "source": [
        "Where to go from here?\n",
        "\n",
        "Read: \n",
        "\n",
        "- Algo Foundations of Differential Privacy\n",
        "- Deep Learrning with DP: arxiv.org/pdf/1607.00133.pdf\n",
        "\n",
        "Topics:\n",
        "\n",
        "- The Exponential Mechanism\n",
        "- The Moment's Accountant\n",
        "- DP SGD\n",
        "\n",
        "Advice:\n",
        "\n",
        "- For deployments - stick with public frameworks!\n",
        "- Join DP Community\n",
        "- Don't get ahead of yourself - DP still in its early days."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ts8iVYJj6dp4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}