PipelineDP is a Python framework for applying differentially private aggregations to large datasets using batch processing systems such as Apache Spark, Apache Beam, and more.
This PR extends explain computation report by adding noise kind and noise parameters for count/privacy_id_count/sum. For other aggregation it will be added later
Now it looks like
Computed DP count with
Gaussian mechanism: parameter=17.64452389929557 eps=0.3333333333333333 delta=3.333333333333333e-07 l2_sensitivity=1.4142135623730951
Computed DP sum with
Gaussian mechanism: parameter=88.22261949647785 eps=0.3333333333333333 delta=3.333333333333333e-07 l2_sensitivity=7.0710678118654755
Computed DP privacy_id_count with
Gaussian mechanism: parameter=17.64452389929557 eps=0.3333333333333333 delta=3.333333333333333e-07 l2_sensitivity=1.4142135623730951
This PR extends explain computation report by adding noise kind and noise parameters for count/privacy_id_count/sum. For other aggregation it will be added later
Now it looks like