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LDP-noise-addition

Overview

This repository contains a Python script for adding Local Differential Privacy (LDP) noise to a dataset. The script demonstrates two types of noise addition methods: Laplacian noise and randomized response. It provides functions to add these noise types to both numerical and categorical columns of the dataset.

Script Details

  • The script provides functions to add Laplacian noise and randomized response noise to numerical and categorical columns, respectively.
  • It utilizes the numpy and pandas libraries for numerical operations and data manipulation.
  • Make sure to specify the privacy parameter eps according to the desired level of privacy for the dataset.
  • The noisy dataset is saved as a new CSV file with the suffix _eps_<epsilon_value>.csv in the Data/ directory.

Usage

  1. Clone the repository to your local machine: 
    git clone https://github.com/your_username/LDP-noise-addition.git
  2. Install the required dependencies: 
    pip install numpy pandas
  3. Place your dataset in the Data/ directory. The script assumes the dataset is in CSV format.
  4. Update the script (add_ldp_noise.py) with the correct path to your dataset and modify the categorical/numerical columns.
  5. Run the script: 
    python add_ldp_noise.py
  6. The script will generate a new CSV file with the noisy dataset in the Data/ directory.

Dataset

The dataset used in this project is the "Adult Census Income" dataset, available from Kaggle. It contains various demographic and financial attributes of individuals.