During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides differential privacy often remains a manual task to be completed by an expert in the field. Language-based techniques have been proposed for fully automating proofs of differential privacy via type system design, however these results have lagged behind advances in differentially-private algorithms, leaving a noticeable gap in programs which can be automatically verified while also providing state-of-the-art bounds on privacy.We propose Duet, an expressive higher-order language, linear type system and tool for automatically verifying differential privacy of general-purpose higher-order programs. In addition to general purpose programming, Duet supports encoding machine learning algorithms such as stochastic gradient descent, as well as common auxiliary data analysis tasks such as clipping, normalization and hyperparameter tuning-each of which are particularly challenging to encode in a statically verified differential privacy framework.We present a core design of the Duet language and linear type system, and complete key proofs about privacy for well-typed programs. We then show how to extend Duet to support realistic machine learning applications and recent variants of differential privacy which result in improved accuracy for many practical differentially private algorithms. Finally, we implement several differentially private machine learning algorithms in Duet which have never before been automatically verified by a language-based tool, and we present experimental results which demonstrate the benefits of Duet's language design in terms of accuracy of trained machine learning models. privacy provides a robust solution to this problem, and as a result, a number of differentially private algorithms have been developed for machine learning [3,13,17,28,42,50,51,54].Few practical approaches exist, however, for automatically proving that a general-purpose program satisfies differential privacy-an increasingly desirable goal, since many machine learning pipelines are expressed as programs that combine existing algorithms with custom code. Enforcing differential privacy for a new program currently requires a new, manually-written privacy proof. This process is arduous, error-prone, and must be performed by an expert in differential privacy (and re-performed, each time the program is modified).We present Duet, a programming language, type system and tool for expressing and statically verifying privacy-preserving programs. Duet supports (1) general purpose programming features like compound datatypes and higher-order functions, (2) library functions for matrix-based computations, and (3) multiple state-of-the-art variants of differential privacy-(ϵ, δ )-differential privacy [25], Rényi differential privacy [38], zero-concentrated differential privacy (zCDP) [16], and truncated-concentrated differential privacy (tCDP) [15]-and can be easily extended to...
UCLID5 is a tool for the multi-modal formal modeling, verification, and synthesis of systems. It enables one to tackle verification problems for heterogeneous systems such as combinations of hardware and software, or those that have multiple, varied specifications, or systems that require hybrid modes of modeling. A novel aspect of UCLID5 is an emphasis on the use of syntax-guided and inductive synthesis to automate steps in modeling and verification. This tool paper presents new developments in the UCLID5 tool including new language features, integration with new techniques for syntax-guided synthesis and satisfiability solving, support for hyperproperties and combinations of axiomatic and operational modeling, demonstrations on new problem classes, and a robust implementation.
UCLID5 is a tool for the multi-modal formal modeling, verification, and synthesis of systems. It enables one to tackle verification problems for heterogeneous systems such as combinations of hardware and software, or those that have multiple, varied specifications, or systems that require hybrid modes of modeling. A novel aspect of UCLID5 is an emphasis on the use of syntax-guided and inductive synthesis to automate steps in modeling and verification. This tool paper presents new developments in the UCLID5 tool including new language features, integration with new techniques for syntax-guided synthesis and satisfiability solving, support for hyperproperties and combinations of axiomatic and operational modeling, demonstrations on new problem classes, and a robust implementation.
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