This directory contains tools for tracking differential privacy budgets, available as part of the Google differential privacy library.
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.
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.
FAQs
Tools for tracking differential privacy budgets
We found that dp-accounting demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
A malicious npm campaign is targeting Ethereum developers by impersonating Hardhat plugins and the Nomic Foundation, stealing sensitive data like private keys.
Research
Security News
Socket researchers uncover a malicious npm package posing as a tool for detecting vulnerabilities in Etherium smart contracts.
Security News
Research
A supply chain attack on Rspack's npm packages injected cryptomining malware, potentially impacting thousands of developers.
