Metadata-Version: 1.0
Name: eeprivacy
Version: 0.0.5
Summary: Energy Differential Privacy
Home-page: http://github.com/openeemeter/eeprivacy
Author: Marc Paré
Author-email: marc@recurve.com
License: UNKNOWN
Description: 
        eeprivacy
        =========
        
        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
        
Platform: UNKNOWN
