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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](https://openeemeter.github.io/eeprivacy/)

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

Installation

pip install eeprivacy

Local Usage

Notebooks

With your preferred notebook environment (like [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) or [nteract](https://nteract.io/)), install eeprivacy and try out any of the [example notebooks](https://openeemeter.github.io/eeprivacy/private-load-shape-algorithm-design.html).

REPL

>>> from eeprivacy.mechanisms import LaplaceMechanism
>>> LaplaceMechanism.execute(value=0, epsilon=0.1, sensitivity=1)
1.198515653814998

Development

Build docs:

./bin/build_docs

Run tests:

./bin/test

Project details


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Files for eeprivacy, version 0.0.5
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Filename, size eeprivacy-0.0.5-py2.py3-none-any.whl (10.4 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size eeprivacy-0.0.5.tar.gz (5.8 kB) File type Source Python version None Upload date Hashes View

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