From this link on Differential Privacy, the L-2 sensitivity is defined as follows:
Question:
Here's my understanding of the above equation:
- Make a set of
D'data sets such thatD'is same asDexcept it has an absence of a one row. For eachD', calculate the given L-2 norm and return the maximum L-2 norm.
In pseudo-code.
REQUIRE: D
INITIALIZE empty list: norms
FOR each row in D:
1. Define D` such that D` is the same as D except it doesn't have "row" in it
2. Calculate norm = ||f(D) - f(D')||_2
3. Store the norm in norms
RETURN max value in norms
- Is my understanding correct?
Background:
For a machine learning task, I'm trying to perform differentially private gradient descent optimization on a data set D by adding noise to the gradient.
In order to add Gaussian noise to the gradient, I need to calculate the L-2 sensitivity first. In my case, f is a vector-valued function (gradient), but I think there's no issue with the definition here.
