Differential privacy (DP) has become the gold standard for privacy-preserving data analysis, but implementing it correctly has proven challenging. Prior work has focused on verifying DP at a high level, assuming either that the foundations are correct or that a perfect source of random noise is available. However, the underlying theory of differential privacy can be very complex and subtle. Flaws in basic mechanisms and random number generation have been a critical source of vulnerabilities in real-world DP systems. In this paper, we present SampCert, the first comprehensive, mechanized foundation for executable implementations of differential privacy. SampCert is written in Lean with over 12,000 lines of proof. It offers a generic and extensible notion of DP, a framework for constructing and composing DP mechanisms, and formally verified implementations of Laplace and Gaussian sampling algorithms. SampCert provides (1) a mechanized foundation for developing the next generation of differentially private algorithms, and (2) mechanically verified primitives that can be deployed in production systems. Indeed, SampCert’s verified algorithms power the DP offerings of Amazon Web Services, demonstrating its real-world impact. SampCert’s key innovations include: (1) A generic DP foundation that can be instantiated for various DP definitions (e.g., pure, concentrated, Rényi DP); (2) formally verified discrete Laplace and Gaussian sampling algorithms that avoid the pitfalls of floating-point implementations; and (3) a simple probability monad and novel proof techniques that streamline the formalization. To enable proving complex correctness properties of DP and random number generation, SampCert makes heavy use of Lean’s extensive Mathlib library, leveraging theorems in Fourier analysis, measure and probability theory, number theory, and topology.

Recent versions of the Haskell compiler GHC have a number of advanced features that allow many idioms from dependently typed programming to be encoded. We describe our experiences using this "dependently typed Haskell" to construct a performance-critical library that is a key component in a number of verification tools. We have discovered that it can be done, and it brings significant value, but also at a high cost. In this experience report, we describe the ways in which programming at the edge of what is expressible in Haskell's type system has brought us value, the difficulties that it has imposed, and some of the ways we coped with the difficulties.

Programs written in multiple languages are known as polyglot programs. In part due to the proliferation of new and productive high-level programming languages, these programs are becoming more common in environments that must interoperate with existing systems. Polyglot programs must manage resource lifetimes across language boundaries. Resource lifetime management bugs can lead to leaks and crashes, which are more difficult to debug in polyglot programs than monoglot programs.

High-level languages are growing in popularity. However, decades of C software development have produced large libraries of fast, time-tested, meritorious code that are impractical to recreate from scratch. Cross-language bindings can expose low-level C code to high-level languages. Unfortunately, writing bindings by hand is tedious and error-prone, while mainstream binding generators require extensive manual annotation or fail to offer the language features that users of modern languages have come to expect.