Probabilistic Relational Reasoning via Metrics

The Fuzz programming language [Reed and Pierce, 2010] uses an elegant linear type system combined with a monad-like type to express and reason about probabilistic sensitivity properties, most notably $ε$-differential privacy. We show how to extend Fuzz to capture more general relational properties of probabilistic programs, with approximate, or $(ε, δ)$-differential privacy serving as a leading example. Our technical contributions are threefold. First, we introduce the categorical notion of comonadic lifting of a monad to model composition properties of probabilistic divergences. Then, we show how to express relational properties in terms of sensitivity properties via an adjunction we call the path construction. Finally, we instantiate our semantics to model the terminating fragment of Fuzz extended with types carrying information about other divergences between distributions.