python-dp 1.1.5

Installation

To install PyDP, use the PiPy package manager:

pip install python-dp

(If you have pip3 separately for Python 3.x, use pip3 install python-dp.)

Examples

Refer to the curated list of tutorials and sample code to learn more about the PyDP library.

You can also get started with an introduction to PyDP (a Jupyter notebook) and the carrots demo (a Python file).

Example: calculate the Bounded Mean

# Import PyDP
import pydp as dp
# Import the Bounded Mean algorithm
from pydp.algorithms.laplacian import BoundedMean

# Calculate the Bounded Mean
# Basic Structure: `BoundedMean(epsilon: float, lower_bound: Union[int, float, None], upper_bound: Union[int, float, None])`
# `epsilon`: a Double, between 0 and 1, denoting the privacy threshold,
#            measures the acceptable loss of privacy (with 0 meaning no loss is acceptable)
x = BoundedMean(epsilon=0.6, lower_bound=1, upper_bound=10)

# If the lower and upper bounds are not specified,
# PyDP automatically calculates these bounds
# x = BoundedMean(epsilon: float)
x = BoundedMean(0.6)

# Calculate the result
# Currently supported data types are integers and floats
# Future versions will support additional data types
# (Refer to https://github.com/OpenMined/PyDP/blob/dev/examples/carrots.py)
x.quick_result(input_data: list)

Learning Resources

Go to resources to learn more about differential privacy.

Support and Community on Slack

If you have questions about the PyDP library, join OpenMined’s Slack and check the #lib_pydp channel. To follow the code source changes, join #code_dp_python.

Contributing

To contribute to the PyDP project, read the guidelines.

Pull requests are welcome. If you want to introduce major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

<!– ## Contributors –>

License

Apache License 2.0