Hypercollecting Semantics and its Application to Static Analysis of Information Flow

TL;DR

This paper introduces a novel abstract interpretation framework for static analysis of information flow, enabling systematic derivation of analyses for hyperproperties, including qualitative and quantitative measures of information leakage.

Contribution

It develops a fixpoint characterization of hypercollecting semantics within abstract interpretation, allowing the derivation of new and existing information flow analyses.

Findings

Derived a dependence analysis similar to prior logic and type systems.

Created a new cardinality analysis for quantitative information flow.

Achieved precision comparable to state-of-the-art static analyses.

Abstract

We show how static analysis for secure information flow can be expressed and proved correct entirely within the framework of abstract interpretation. The key idea is to define a Galois connection that directly approximates the hyperproperty of interest. To enable use of such Galois connections, we introduce a fixpoint characterisation of hypercollecting semantics, i.e. a "set of set" transformer. This makes it possible to systematically derive static analyses for hyperproperties entirely within the calculational framework of abstract interpretation. We evaluate this technique by deriving example static analyses. For qualitative information flow, we derive a dependence analysis similar to the logic of Amtoft and Banerjee (SAS'04) and the type system of Hunt and Sands (POPL'06). For quantitative information flow, we derive a novel cardinality analysis that bounds the leakage conveyed by a program instead of simply deciding whether it exists. This encompasses problems that are hypersafety but not k-safety. We put the framework to use and introduce variations that achieve precision rivalling the most recent and precise static analyses for information flow.