Contextual Linear Types for Differential Privacy [article]

Matías Toro, David Darais, Chike Abuah, Joe Near, Damián Árquez, Federico Olmedo, Éric Tanter
2021 arXiv   pre-print
Language support for differentially-private programming is both crucial and delicate. While elaborate program logics can be very expressive, type-system based approaches using linear types tend to be more lightweight and amenable to automatic checking and inference, and in particular in the presence of higher-order programming. Since the seminal design of Fuzz, which is restricted to ϵ-differential privacy, a lot of effort has been made to support more advanced variants of differential privacy,
more » ... like (ϵ,δ)-differential privacy. However, supporting these advanced privacy variants while also supporting higher-order programming in full has been proven to be challenging. We present Jazz, a language and type system which uses linear types and latent contextual effects to support both advanced variants of differential privacy and higher-order programming. Even when avoiding advanced variants and higher-order programming, our system achieves higher precision than prior work for a large class of programming patterns. We formalize the core of the Jazz language, prove it sound for privacy via a logical relation for metric preservation, and illustrate its expressive power through a number of case studies drawn from the recent differential privacy literature.
arXiv:2010.11342v2 fatcat:gwgcsvx2mzewlpui56256g4noa