Need help with differential-privacy-library?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

IBM
361 Stars 86 Forks MIT License 450 Commits 6 Opened issues

Description

Diffprivlib: The IBM Differential Privacy Library

Services available

!
?

Need anything else?

Contributors list

Diffprivlib v0.3

Python versions Downloads PyPi version PyPi status Build Status Documentation Status Language grade: Python

Diffprivlib is a general-purpose library for experimenting with, investigating and developing applications in, differential privacy.

Use diffprivlib if you are looking to:

  • Experiment with differential privacy
  • Explore the impact of differential privacy on machine learning accuracy using classification and clustering models
  • Build your own differential privacy applications, using our extensive collection of mechanisms

Diffprivlib supports Python versions 3.6 to 3.8.

Getting started: Machine learning with differential privacy in 30 seconds

We're using the Iris dataset, so let's load it and perform an 80/20 train/test split.

from sklearn import datasets
from sklearn.model_selection import train_test_split

dataset = datasets.load_iris() X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2)

Now, let's train a differentially private naive Bayes classifier. Our classifier runs just like an

sklearn
classifier, so you can get up and running quickly.

diffprivlib.models.GaussianNB
can be run without any parameters, although this will throw a warning (we need to specify the
bounds
parameter to avoid this). The privacy level is controlled by the parameter
epsilon
, which is passed to the classifier at initialisation (e.g.
GaussianNB(epsilon=0.1)
). The default is
epsilon = 1.0
.
from diffprivlib.models import GaussianNB

clf = GaussianNB() clf.fit(X_train, y_train)

We can now classify unseen examples, knowing that the trained model is differentially private and preserves the privacy of the 'individuals' in the training set (flowers are entitled to their privacy too!).

clf.predict(X_test)

Every time the model is trained with

.fit()
, a different model is produced due to the randomness of differential privacy. The accuracy will therefore change, even if it's re-trained with the same training data. Try it for yourself to find out!
print("Test accuracy: %f" % clf.score(X_test, y_test))

We can easily evaluate the accuracy of the model for various

epsilon
values and plot it with
matplotlib
.
import numpy as np
import matplotlib.pyplot as plt

epsilons = np.logspace(-2, 2, 50) bounds = ([4.3, 2.0, 1.1, 0.1], [7.9, 4.4, 6.9, 2.5]) accuracy = list()

for epsilon in epsilons: clf = GaussianNB(bounds=bounds, epsilon=epsilon) clf.fit(X_train, y_train)

accuracy.append(clf.score(X_test, y_test))

plt.semilogx(epsilons, accuracy) plt.title("Differentially private Naive Bayes accuracy") plt.xlabel("epsilon") plt.ylabel("Accuracy") plt.show()

Differentially private naive Bayes

Congratulations, you've completed your first differentially private machine learning task with the Differential Privacy Library! Check out more examples in the notebooks directory, or dive straight in.

Contents

Diffprivlib is comprised of four major components: 1. Mechanisms: These are the building blocks of differential privacy, and are used in all models that implement differential privacy. Mechanisms have little or no default settings, and are intended for use by experts implementing their own models. They can, however, be used outside models for separate investigations, etc. 1. Models: This module includes machine learning models with differential privacy. Diffprivlib currently has models for clustering, classification, regression, dimensionality reduction and pre-processing. 1. Tools: Diffprivlib comes with a number of generic tools for differentially private data analysis. This includes differentially private histograms, following the same format as Numpy's histogram function. 1. Accountant: The

BudgetAccountant
class can be used to track privacy budget and calculate total privacy loss using advanced composition techniques.

Setup

Installation with
pip

The library is designed to run with Python 3. The library can be installed from the PyPi repository using

pip
(or
pip3
):
pip install diffprivlib

Manual installation

For the most recent version of the library, either download the source code or clone the repository in your directory of choice:

git clone https://github.com/IBM/differential-privacy-library

To install

diffprivlib
, do the following in the project folder (alternatively, you can run
python3 -m pip install .
):
bash
pip install .

The library comes with a basic set of unit tests for

pytest
. To check your install, you can run all the unit tests by calling
pytest
in the install folder:
pytest

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.