eeprivacy 0.0.5

Energy Differential Privacy

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/)

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

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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