How can I implement differential privacy in Python?

How can I implement differential privacy in Python?

You can implement differential privacy in Python using libraries like Google’s Differential Privacy library or PySyft. Would you like a code example?

Yes, please provide a code example.

Here's a simple example using the Google Differential Privacy library:

from diffprivlib.models import GaussianNB

# Sample data
X = [[0, 0], [1, 1], [0, 1], [1, 0]]
y = [0, 1, 1, 0]

# Create a Gaussian Naive Bayes model with differential privacy
model = GaussianNB(epsilon=1.0)
model.fit(X, y)

This code trains a Gaussian Naive Bayes model with differential privacy. Would you like to know more about the parameters?

What does the epsilon parameter mean?

The epsilon parameter controls the level of privacy. A smaller epsilon means more privacy but less accuracy, while a larger epsilon allows for more accurate results but less privacy.

Are there any other libraries I should consider?

Yes, you might also consider libraries like PySyft, TensorFlow Privacy, or Opacus for implementing differential privacy in various contexts.