Relational Reasoning via Probabilistic Coupling

被引：20

作者：

Barthe, Gilles ^{[1
]}

Espitau, Thomas ^{[1
,2
]}

Gregoire, Benjamin ^{[3
]}

Hsu, Justin ^{[4
]}

Stefanesco, Leo ^{[1
,5
]}

Strub, Pierre-Yves ^{[1
]}

机构：

[1] IMDEA Software, Madrid, Spain

[2] ENS Cachan, Cachan, France

[3] Inria, Sophia Antipolis, France

[4] Univ Penn, Philadelphia, PA 19104 USA

[5] ENS Lyon, Lyon, France

来源：

LOGIC FOR PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND REASONING, (LPAR-20 2015) | 2015年 / 9450卷

关键词：

D O I：

10.1007/978-3-662-48899-7_27

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Probabilistic coupling is a powerful tool for analyzing pairs of probabilistic processes. Roughly, coupling two processes requires finding an appropriate witness process that models both processes in the same probability space. Couplings are powerful tools proving properties about the relation between two processes, include reasoning about convergence of distributions and stochastic dominance-a probabilistic version of a monotonicity property. While the mathematical definition of coupling looks rather complex and cumbersome to manipulate, we show that the relational program logic pRHL-the logic underlying the EasyCrypt cryptographic proof assistant-already internalizes a generalization of probabilistic coupling. With this insight, constructing couplings is no harder than constructing logical proofs. We demonstrate how to express and verify classic examples of couplings in pRHL, and we mechanically verify several couplings in EasyCrypt.

引用

页码：387 / 401

页数：15