Privacy measure with meaning defined by an OpenDP Library user (you).

Any two instances of UserDivergence are equal if their string descriptors are equal.

Usage

user_divergence(descriptor)

Arguments

descriptor

A string description of the privacy measure.

Value

Measure

Details

Required features: honest-but-curious

Why honest-but-curious?:

The essential requirement of a privacy measure is that it is closed under postprocessing. Your privacy measure D must satisfy that, for any pure function f and any two distributions Y, Y', then \(D(Y, Y') \ge D(f(Y), f(Y'))\).

Beyond this, you should also consider whether your privacy measure can be used to provide meaningful privacy guarantees to your privacy units.