Skip to main content

Energy Differential Privacy

Project description

This repository contains the pilot implementation of the core privacy methods for Energy Differential Privacy (EDP). The key components are:

  • Core Differential Privacy for energy efficiency analytics (eeprivacy)

  • Python API documentation for eeprivacy

  • Sample implementations of key use cases

[Examples and library documentation](

Energy Differential Privacy (EDP) enables the use of the gold standard of privacy protection, differential privacy, for high value energy efficiency analytics.


pip install eeprivacy

Local Usage


With your preferred notebook environment (like [JupyterLab]( or [nteract](, install eeprivacy and try out any of the [example notebooks](


>>> from eeprivacy.mechanisms import LaplaceMechanism
>>> LaplaceMechanism.execute(value=0, epsilon=0.1, sensitivity=1)


Build docs:


Run tests:


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

eeprivacy-0.0.5.tar.gz (5.8 kB view hashes)

Uploaded source

Built Distribution

eeprivacy-0.0.5-py2.py3-none-any.whl (10.4 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page