# 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).
The set of DpEvent classes allow you to describe complex differentially private
mechanisms such as Laplace and Gaussian, subsampling mechanisms, and their
compositions. The PrivacyAccountant classes can ingest DpEvents and return the
ε, δ of the composite mechanism. Privacy Loss Distributions (PLDs) and RDP
accounting are currently supported.
More detailed definitions and references about PLDs can be found
[in our supplementary pdf document](https://github.com/google/differential-privacy/tree/main/common_docs/Privacy_Loss_Distributions.pdf).
Our library only support Python version >= 3.9. We test this library on Linux
with Python version 3.9. If you experience any problems, please file an issue on
GitHub, also for other platforms or Python versions.
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