Metadata-Version: 2.1
Name: dp-accounting
Version: 0.1.0
Summary: Tools for tracking differential privacy budgets
Home-page: https://github.com/google/differential-privacy/
Author: Google Differential Privacy Team
Author-email: dp-open-source@google.com
License: Apache 2.0
Keywords: differential-privacy accounting
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# Differential Privacy Accounting

This directory contains tools for tracking differential privacy budgets,
available as part of the
[Google differential privacy library](https://github.com/google/differential-privacy).
Currently, it provides an implementation of Privacy Loss Distributions (PLDs)
which can help compute an accurate estimate of the total ε, δ across multiple
executions of differentially private aggregations. Our implementation currently
supports Laplace mechanisms, Gaussian mechanisms and randomized response. More
detailed definitions and references can be found
[in our supplementary pdf document](https://github.com/google/differential-privacy/tree/main/common_docs/Privacy_Loss_Distributions.pdf).

We test this library on Linux with Python version 3.7. If you experience any
problems, please file an issue on GitHub, also for other platforms or Python
versions.

