CRLOPLApr 18, 2016

Compositional security and collateral leakage

arXiv:1604.04983v11 citations
Originality Incremental advance
AI Analysis

This addresses a foundational issue in program security for developers and theorists, though it is incremental as it builds on existing models.

The paper tackles the problem of collateral leakage in quantitative information flow, where program executions can inadvertently leak correlated secrets from broader contexts, and shows that addressing this is necessary for compositional semantics, providing techniques to calculate and bound such leakage with examples like public-key encryption.

In quantitative information flow we say that program $Q$ is "at least as secure as" $P$ just when the amount of secret information flowing from $Q$ is never more than flows from $P$, with of course a suitable quantification of "flow". This secure-refinement order $\sqsubseteq$ is compositional just when $P{\sqsubseteq}Q$ implies ${\cal C}(P){\sqsubseteq}{\cal C}(Q)$ for any context ${\cal C}$, again with a suitable definition of "context". Remarkable however is that leaks caused by executing $P,Q$ might not be limited to their declared variables: they might impact correlated secrets in variables declared and initialised in some broader context to which $P,Q$ do not refer even implicitly. We call such leaks collateral because their effect is felt in domains of which (the programmers of) $P, Q$ might be wholly unaware: our inspiration is the "Dalenius" phenomenon for statistical databases. We show that a proper treatment of these collateral leaks is necessary for a compositional program semantics for read/write "open" programs. By adapting a recent Hidden-Markov denotational model for non-interference security, so that it becomes "collateral aware", we give techniques and examples (e.g.\ public-key encryption) to show how collateral leakage can be calculated and then bounded in its severity.

Foundations

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