Bunched Fuzz: Sensitivity for Vector Metrics

"Program sensitivity" measures the distance between the outputs of a program when it is run on two related inputs. This notion, which plays an important role in areas such as data privacy and optimization, has been the focus of several program analysis techniques introduced in recent years. One approach that has proved particularly fruitful for this domain is the use of 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 notion of distance, and the typing rules explain how those distances can be treated soundly when analyzing the sensitivity of a program. In particular, Fuzz features two products types, corresponding to two different sensitivity analyses: 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 products in Fuzz 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 simplify 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 important examples of metrics between probability distributions in a natural way.