Statistical Verification of Quantitative Hyperproperties: Beyond Boolean Quantification

2026-08-04

AI Summary

This work addresses the limitations of existing formalisms for hyperproperties in capturing quantitative aspects inherent in real-world systems, such as numerical relationships in information flow control. To overcome this, the paper introduces Quantitative Hyper-Logic (QHL), a novel framework that reformulates hyperproperty specifications using measure theory, replacing classical Boolean quantifiers with measures to support nested quantitative structures. Leveraging Hoeffding’s inequality and extreme value theory, the authors develop an efficient statistical verification algorithm and provide rigorous analyses of sample complexity and statistical guarantees. Experimental evaluation on quantitative information-flow benchmarks demonstrates that QHL substantially outperforms conventional qualitative approaches, offering superior expressiveness and verification capabilities that better align with the demands of practical systems.

Abstract

Formalisms for hyperproperties provide a solid foundation for studying the verification problem across classes of relational properties, such as those in information flow control (IFC). However, existing formalisms remain limited in expressiveness when it comes to capturing practical aspects of real-world systems. In particular, they do not adequately account for the quantitative nature of such systems. In this paper, we address this gap by revisiting the specification and verification of hyperproperties from a quantitative, measure-based, perspective. We introduce Quantitative Hyper-Logic (QHL), which replaces qualitative trace quantifiers with measure-based ones and extends temporal predicates with richer quantitative expressions. We further study the verification problem from a statistical verification point of view, and develop algorithms for the statistical verification of QHL specifications. For the introduced measure-based quantifiers, we particularly provide an analysis in terms of sample complexity and achievable statistical guarantees. In particular, we show how statistical methods such as Hoeffding's inequality and extreme value theory can be combined to develop statistical verification algorithms for nested measure-based quantifiers. Our approach provides quantitative alternatives for where traditional verification methods become infeasible. We demonstrate both expressiveness and efficacy on benchmarks from quantitative IFC, comparing against qualitative methods.

Problem

• hyperproperties
• quantitative verification
• information flow control
• measure-based quantification
• statistical verification

Innovation

• Quantitative Hyperproperties
• Statistical Verification
• Measure-based Quantifiers
• Hyper-Logic
• Information Flow Control