  Program sensitivity measures the distance between the outputs of a program
when run on two related inputs. This notion, which plays a key role in areas
such as data privacy and optimization, has been the focus of several program
analysis techniques introduced in recent years. Among the most successful ones,
we can highlight type systems inspired by linear logic, as pioneered by Reed
and Pierce in the Fuzz programming language. In Fuzz, each type is equipped
with its own distance, and sensitivity analysis boils down to type checking. In
particular, Fuzz features two product types, corresponding to two different
notions of distance: the tensor product combines the distances of each
component by adding them, while the with product takes their maximum.
  In this work, we show that these products can be generalized to arbitrary
$L^p$ distances, metrics that are often used in privacy and optimization. The
original Fuzz products, tensor and with, correspond to the special cases $L^1$
and $L^\infty$. To ease the handling of such products, we extend the Fuzz type
system with bunches -- as in the logic of bunched implications -- where the
distances of different groups of variables can be combined using different
$L^p$ distances. We show that our extension can be used to reason about
quantitative properties of probabilistic programs.
