Solo: a lightweight static analysis for differential privacy
Chike Abuah, David Darais, Joseph P. Near
Abstract
Existing approaches for statically enforcing differential privacy in higher order languages use either linear or relational refinement types. A barrier to adoption for these approaches is the lack of support for expressing these "fancy types" in mainstream programming languages. For example, no mainstream language supports relational refinement types, and although Rust and modern versions of Haskell both employ some linear typing techniques, they are inadequate for embedding enforcement of differential privacy, which requires "full" linear types a la Girard. We propose a new type system that enforces differential privacy, avoids the use of linear and relational refinement types, and can be easily embedded in mainstream richly typed programming languages such as Scala, OCaml and Haskell. We demonstrate such an embedding in Haskell, demonstrate its expressiveness on case studies, and prove soundness of our type-based enforcement of differential privacy.
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Cited by top-tier papers2
- Sensitivity by ParametricityElisabet Lobo Vesga, Alejandro Russo, Marco Gaboardi, Carlos Tomé CortiñasOOPSLA 2024 · 2 citations
- Modular Verification of Differential Privacy in Probabilistic Higher-Order Separation LogicPhilipp G. Haselwarter, Alejandro Aguirre, Simon Oddershede Gregersen, Kwing Hei Li et al.PLDI 2026
Builds on5
- Detecting Violations of Differential PrivacyZeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang et al.CCS 2018 · 156 citations
- DP-Finder: Finding Differential Privacy Violations by Sampling and OptimizationBenjamin Bichsel, Timon Gehr, Dana Drachsler-Cohen, Petar Tsankov et al.CCS 2018 · 82 citations
- ALCHEMY: A Language and Compiler for Homomorphic Encryption Made easYEric Crockett, Chris Peikert, Chad SharpCCS 2018 · 68 citations
- A Programming Framework for Differential Privacy with Accuracy Concentration BoundsElisabet Lobo Vesga, Alejandro Russo, Marco GaboardiS&P 2020 · 32 citations
- CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise CounterexamplesYuxin Wang, Zeyu Ding, Daniel Kifer, Danfeng ZhangCCS 2020 · 31 citations
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