一句话：提出 Local Fuzz：带依赖余效应的类型系统，刻画程序局部敏感性，给出优于 Fuzz 的差分隐私保证。
差分隐私通过界定查询的敏感度（输入变化对输出的影响）并据此加噪来保证隐私。已有的 Fuzz 类型系统只能刻画全局敏感度，而许多有用查询的全局敏感度为无穷，限制了其适用范围。本文引入 Local Fuzz，一个带依赖余效应（dependent coeffects）的类型系统，用于界定简单函数式语言程序的局部敏感度，即相对于某个特定数据集的相邻输入所引起的输出变化。作者在扩展前度量空间（extended premetric spaces）范畴中给出该系统的指称语义，借助新近提出的依赖分级 comonad 构造完成语义建模。文章进一步说明，对于依赖全局敏感度的机制，以及 Propose-Test-Release 等利用局部敏感度的机制，Local Fuzz 都能导出比 Fuzz 更好的差分隐私保证。
Differential privacy is a formal definition of privacy that bounds the maximum acceptable information leakage when a query is performed on sensitive data. To ensure this property, a key technique involves bounding the query’s sensitivity (how much input variations affect the output) and adding noise to the result according to this quantity. While prior work like the Fuzz type system focuses on global sensitivity, many useful queries have infinite global sensitivity, restricting the scope of such approaches. This limitation can be addressed by considering a more fine-grained measure: local sensitivity, which quantifies output change for inputs adjacent to a specific dataset. In this article, we introduce Local Fuzz, a type system with dependent coeffects designed to bound the local sensitivity of programs written in a simple functional language. We provide a denotational semantics for this system in the category of extended premetric spaces, leveraging the recently introduced construction of a dependently graded comonad. Finally, we illustrate how Local Fuzz can lead to better differential privacy guarantees than Fuzz, both for mechanisms that rely on global sensitivity and for those that leverage local sensitivity, such as the Propose-Test-Release framework.
@article{SannierB26,
title = {Dependent Coeffects for Local Sensitivity Analysis},
author = {Victor Sannier and Patrick Baillot},
journal = {Proceedings of the ACM on Programming Languages},
volume = {10},
number = {POPL},
year = {2026},
doi = {10.1145/3776670}
}