As cryptographic proofs have become essentially unverifiable, cryptographers have argued
in favor of developing techniques that help tame the complexity of their proofs. Game-based …

We want assurances that sensitive information will not be disclosed when aggregate data
derived from a database is published. Differential privacy offers a strong statistical guarantee …

We study the semantic foundation of expressive probabilistic programming languages, that
support higher-order functions, continuous distributions, and soft constraints (such as …

Differential privacy offers a way to answer queries about sensitive information while
providing strong, provable privacy guarantees, ensuring that the presence or absence of a …

We show that a measure-based denotational semantics for probabilistic programming is
commutative. The idea underlying probabilistic programming languages (Anglican, Church …

We present Hakaru, a new probabilistic programming system that allows composable reuse
of distributions, queries, and inference algorithms, all expressed in a single language of …

Differential privacy is a notion of confidentiality that protects the privacy of individuals while
allowing useful computations on their private data. Deriving differential privacy guarantees …

Emerging applications increasingly use estimates such as sensor data (GPS), probabilistic
models, machine learning, big data, and human data. Unfortunately, representing this …

This chapter illustrates the use of probabilistic techniques in natural language pragmatics
and semantics with a concrete formal model. This model shows that a probabilistic …

We develop the operational semantics of an untyped probabilistic λ-calculus with continuous
distributions, and both hard and soft constraints, as a foundation for universal probabilistic …