Tools for tracking differential privacy budgets
Differential Privacy Accounting
This directory contains tools for tracking differential privacy budgets, available as part of the Google differential privacy library. 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.
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.
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