Welcome to the IBM Differential Privacy Library

This is a library dedicated to differential privacy and machine learning. Its purpose is to allow experimentation, simulation, and implementation of differentially private models using a common codebase and building blocks.

Modules

• diffprivlib.accountant
  • Base class
• diffprivlib.mechanisms
  • Base classes
  • Binary mechanism
  • Bingham mechanism
  • Exponential mechanisms
  • Gaussian mechanisms
  • Geometric mechanisms
  • Laplace mechanisms
  • Staircase mechanism
  • Uniform mechanism
  • Vector mechanism
• diffprivlib.mechanisms.transforms
  • Base class
  • Type casting transforms
  • Other transforms
• diffprivlib.tools
  • Histogram functions
  • General Utilities
  • Quantile-like functions
• diffprivlib.models
  • Classification models
    • Gaussian Naive Bayes
    • Logistic Regression
    • Random Forest
  • Regression models
    • Linear Regression
  • Clustering models
    • K-Means
  • Dimensionality reduction models
    • PCA
  • Preprocessing
    • Standard Scaler
• Utilities and general functions
  • Exceptions and warnings
  • General classes
  • General functions
• Validation functions
  • General functions

Indices and tables

• Index

• Module Index

• Search Page