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josecastillolema/dpc_ej5.csv

Last active March 12, 2025 18:28
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dpc_ej5.csv
Name Age Blood
Mary Clan 82 A+
Jane Doe 46 AB+
Ted Coe 50 B-
Bill Murray 44 A-
Clint East 52 B+
Spencer Tracy 79 B+
Bill Ted 56 0+
Bob Smith 66 A-
Bill Cosby 81 A+
Display the source blob
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Raw
dpc_ej5.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 16673,
"status": "ok",
"timestamp": 1741802483081,
"user": {
"displayName": "JOSE CASTILLO LEMA",
"userId": "16693213845281515939"
},
"user_tz": -60
},
"id": "m5iGpu-6g6p1",
"outputId": "fac556c9-85c9-46f1-945f-ce1f8adcb6f4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting anjana\n",
" Downloading anjana-1.1.0-py3-none-any.whl.metadata (11 kB)\n",
"Collecting beartype==0.19.0 (from anjana)\n",
" Downloading beartype-0.19.0-py3-none-any.whl.metadata (32 kB)\n",
"Requirement already satisfied: docutils==0.21.2 in /usr/local/lib/python3.11/dist-packages (from anjana) (0.21.2)\n",
"Collecting numpy==2.0.2 (from anjana)\n",
" Downloading numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.9/60.9 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: pandas==2.2.2 in /usr/local/lib/python3.11/dist-packages (from anjana) (2.2.2)\n",
"Collecting pycanon==1.0.3 (from anjana)\n",
" Downloading pycanon-1.0.3-py3-none-any.whl.metadata (8.5 kB)\n",
"Requirement already satisfied: typing_extensions==4.12.2 in /usr/local/lib/python3.11/dist-packages (from anjana) (4.12.2)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas==2.2.2->anjana) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas==2.2.2->anjana) (2025.1)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas==2.2.2->anjana) (2025.1)\n",
"Collecting reportlab==4.2.5 (from pycanon==1.0.3->anjana)\n",
" Downloading reportlab-4.2.5-py3-none-any.whl.metadata (1.5 kB)\n",
"Collecting tabulate==0.8.10 (from pycanon==1.0.3->anjana)\n",
" Downloading tabulate-0.8.10-py3-none-any.whl.metadata (25 kB)\n",
"Collecting typer==0.15.1 (from pycanon==1.0.3->anjana)\n",
" Downloading typer-0.15.1-py3-none-any.whl.metadata (15 kB)\n",
"Requirement already satisfied: pillow>=9.0.0 in /usr/local/lib/python3.11/dist-packages (from reportlab==4.2.5->pycanon==1.0.3->anjana) (11.1.0)\n",
"Requirement already satisfied: chardet in /usr/local/lib/python3.11/dist-packages (from reportlab==4.2.5->pycanon==1.0.3->anjana) (5.2.0)\n",
"Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.11/dist-packages (from typer==0.15.1->pycanon==1.0.3->anjana) (8.1.8)\n",
"Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.11/dist-packages (from typer==0.15.1->pycanon==1.0.3->anjana) (1.5.4)\n",
"Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.11/dist-packages (from typer==0.15.1->pycanon==1.0.3->anjana) (13.9.4)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas==2.2.2->anjana) (1.17.0)\n",
"Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->typer==0.15.1->pycanon==1.0.3->anjana) (3.0.0)\n",
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->typer==0.15.1->pycanon==1.0.3->anjana) (2.18.0)\n",
"Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer==0.15.1->pycanon==1.0.3->anjana) (0.1.2)\n",
"Downloading anjana-1.1.0-py3-none-any.whl (22 kB)\n",
"Downloading beartype-0.19.0-py3-none-any.whl (1.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m27.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (19.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.5/19.5 MB\u001b[0m \u001b[31m59.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading pycanon-1.0.3-py3-none-any.whl (33 kB)\n",
"Downloading reportlab-4.2.5-py3-none-any.whl (1.9 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m54.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading tabulate-0.8.10-py3-none-any.whl (29 kB)\n",
"Downloading typer-0.15.1-py3-none-any.whl (44 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.9/44.9 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: tabulate, reportlab, numpy, beartype, typer, pycanon, anjana\n",
" Attempting uninstall: tabulate\n",
" Found existing installation: tabulate 0.9.0\n",
" Uninstalling tabulate-0.9.0:\n",
" Successfully uninstalled tabulate-0.9.0\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 1.26.4\n",
" Uninstalling numpy-1.26.4:\n",
" Successfully uninstalled numpy-1.26.4\n",
" Attempting uninstall: typer\n",
" Found existing installation: typer 0.15.2\n",
" Uninstalling typer-0.15.2:\n",
" Successfully uninstalled typer-0.15.2\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"bigframes 1.38.0 requires tabulate>=0.9, but you have tabulate 0.8.10 which is incompatible.\n",
"pytensor 2.27.1 requires numpy<2,>=1.17.0, but you have numpy 2.0.2 which is incompatible.\n",
"thinc 8.2.5 requires numpy<2.0.0,>=1.19.0; python_version >= \"3.9\", but you have numpy 2.0.2 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed anjana-1.1.0 beartype-0.19.0 numpy-2.0.2 pycanon-1.0.3 reportlab-4.2.5 tabulate-0.8.10 typer-0.15.1\n"
]
}
],
"source": [
"# Install and import the libraries (pandas, anjana)\n",
"import pandas as pd\n",
"\n",
"!pip install anjana\n",
"import anjana\n",
"from anjana.anonymity import k_anonymity, l_diversity, t_closeness, utils"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 332
},
"executionInfo": {
"elapsed": 58,
"status": "ok",
"timestamp": 1741803699561,
"user": {
"displayName": "JOSE CASTILLO LEMA",
"userId": "16693213845281515939"
},
"user_tz": -60
},
"id": "Cwi5cTBihLEO",
"outputId": "f5f89f0d-af0d-4d26-af52-45ae55861115"
},
"outputs": [
{
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"text/plain": [
" Name Age Blood\n",
"0 Mary Clan 82 A+\n",
"1 Jane Doe 46 AB+\n",
"2 Ted Coe 50 B-\n",
"3 Bill Murray 44 A-\n",
"4 Clint East 52 B+\n",
"5 Spencer Tracy 79 B+\n",
"6 Bill Ted 56 0+\n",
"7 Bob Smith 66 A-\n",
"8 Bill Cosby 81 A+"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Import and visualize the data\n",
"data = pd.read_csv(\"dpc_ej5.csv\")\n",
"data\n",
"\n",
"data.columns = data.columns.str.strip()\n",
"cols = [\n",
" \"Name\",\n",
" \"Age\",\n",
" \"Blood\",\n",
"]\n",
"for col in [\"Name\", \"Blood\"]:\n",
" data[col] = data[col].str.strip()\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"executionInfo": {
"elapsed": 10,
"status": "ok",
"timestamp": 1741802995030,
"user": {
"displayName": "JOSE CASTILLO LEMA",
"userId": "16693213845281515939"
},
"user_tz": -60
},
"id": "vIi2bze9hfj7"
},
"outputs": [],
"source": [
"# Define the identifiers, quasi-identifiers and the sensitive attribute\n",
"quasi_ident = [\n",
" \"Age\",\n",
"]\n",
"ident = [\"Name\"]\n",
"sens_att = [\"Blood\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"executionInfo": {
"elapsed": 4,
"status": "ok",
"timestamp": 1741802709905,
"user": {
"displayName": "JOSE CASTILLO LEMA",
"userId": "16693213845281515939"
},
"user_tz": -60
},
"id": "grkz0WGeh8aj"
},
"outputs": [],
"source": [
"# Define the hierarchies\n",
"age = data['Age'].values\n",
"hierarchies = {\n",
" \"Age\": {\n",
" 0: data[\"Age\"].values,\n",
" 1: utils.generate_intervals(data[\"Age\"].values, 30, 90, 20),\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 332
},
"executionInfo": {
"elapsed": 63,
"status": "ok",
"timestamp": 1741803020854,
"user": {
"displayName": "JOSE CASTILLO LEMA",
"userId": "16693213845281515939"
},
"user_tz": -60
},
"id": "z3Zbiy8NiHAY",
"outputId": "b2fd6326-9057-4683-d6eb-1cb96f3175d1"
},
"outputs": [
{
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"text/plain": [
" Name Age Blood\n",
"0 * [70, 90) A+\n",
"1 * [30, 50) AB+\n",
"2 * [30, 50) B-\n",
"3 * [30, 50) A-\n",
"4 * [50, 70) B+\n",
"5 * [70, 90) B+\n",
"6 * [50, 70) 0+\n",
"7 * [50, 70) A-\n",
"8 * [70, 90) A+"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Apply the algorithm\n",
"k=3\n",
"supp_level=0\n",
"data_anon=k_anonymity(data, ident, quasi_ident, k, supp_level, hierarchies)\n",
"data_anon\n"
]
}
],
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