All critical systems must evolve to meet the needs of a growing and diversifying user base. But supporting that evolution is challenging at increasing scale: Maintainers must find a way to ensure that each change does only what is intended, and will not inadvertently change behavior for existing users. This paper presents how we addressed this challenge for the Amazon Web Services (AWS) authorization engine, invoked 1 billion times per second, by using formal verification. Over a period of four years, we built a new authorization engine, one that behaves functionally the same as its predecessor, using the verification-aware programming language Dafny. We can now confidently deploy enhancements and optimizations while maintaining the highest assurance of both correctness and backward compatibility. We deployed the new engine in 2024 without incident and customers immediately enjoyed a threefold performance improvement. The methodology we followed to build this new engine was not an off-the-shelf application of an existing verification tool, and this paper presents several key insights: 1) Rather than prove correct the existing engine, written in Java, we found it more effective to write a new engine in Dafny, a language built for verification from the ground up, and then compile the result to Java. 2) To ensure performance, debuggability, and to gain trust from stakeholders, we needed to generate readable, idiomatic Java code, essentially a transliteration of the source Dafny. 3) To ensure that the specification matches the system's actual behavior, we performed extensive differential and shadow testing throughout the development process, ultimately comparing against 1015production samples prior to deployment. Our approach demonstrates how formal verification can be effectively applied to evolve critical legacy software at scale.

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.

Internet search has become the first stop in many users' health journeys. Today, about 1 in 20 Google searches are related to healthcare. These queries span a broad range of information needs as people are looking for possible conditions related to their symptoms and are seeking to understand their diagnoses and prescribed treatments, decipher their test results, find pathways of self-care as well as connect to people with similar experiences.