In this code tutorial, I show the difference between local and global sensitivity, and program from scratch how to calculate local sensitivity for both the bounded and unbounded definitions of differential privacy.
In this code tutorial, I show how to implement empirically the calculation of the global sensitivity for the bounded and unbounded definitions of differential privacy.
In this code tutorial, we implement differential identifiability, a differential privacy definition produced by Jaewoo Lee et al. This definitions helps practitioners to decide in a more intuitive manner what the value of epsilon should be, a major problem in the field.
The authors of the paper (Jaewoo Lee et al.) behind this code tutorial proposed bounds for epsilon so that its value may not yield a random output query result that leads to a posterior that is greater than the disclosure risk. In this post, we code their solution.
