Differential_Privacy_for_Secure_Data_Sharing.ipynb

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  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "authorship_tag": "ABX9TyOMHr/UDQaUN+gSvSKCiaWw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStardust/blob/main/Differential_Privacy_for_Secure_Data_Sharing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install opacus"
      ],
      "metadata": {
        "id": "SkzBl_P6h9B8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SSlqMArVhsKm"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from opacus import PrivacyEngine\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "# Define a simple neural network\n",
        "class SimpleNet(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(SimpleNet, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, 64)\n",
        "        self.fc2 = nn.Linear(64, output_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        return self.fc2(x)\n",
        "\n",
        "# Create a dataset and dataloader for demonstration purposes\n",
        "inputs = torch.randn(1024, 10)\n",
        "labels = torch.randint(0, 2, (1024,))\n",
        "dataset = TensorDataset(inputs, labels)\n",
        "train_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "# Training with differential privacy\n",
        "model = SimpleNet(input_dim=10, output_dim=2)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Initialize Privacy Engine without parameters\n",
        "privacy_engine = PrivacyEngine()\n",
        "\n",
        "# Attach Privacy Engine to the optimizer, model, and dataloader\n",
        "model, optimizer, train_loader = privacy_engine.make_private(\n",
        "    module=model,\n",
        "    optimizer=optimizer,\n",
        "    data_loader=train_loader,\n",
        "    noise_multiplier=1.1,\n",
        "    max_grad_norm=1.0,\n",
        ")\n",
        "\n",
        "# Dummy training loop with privacy guarantees\n",
        "for epoch in range(5):\n",
        "    for inputs, labels in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "# Calculate privacy spent using the accountant\n",
        "epsilon = privacy_engine.accountant.get_epsilon(delta=1e-5)\n",
        "print(f\"Model trained with differential privacy, ε: {epsilon:.2f}\")"
      ]
    }
  ]
}