Correlated Secrets in Quantitative Information Flow

A fundamental challenge in controlling the leakage of sensitive information by computer systems is the possibility of correlations between different secrets, with the result that leaking information about one secret may also leak information about a different secret. We explore such leakage, here called Dalenius leakage, within the context of the g-leakage family of leakage measures. We prove a fundamental equivalence between Dalenius min-entropy leakage under arbitrary correlations and g-leakage under arbitrary gain functions, and show how this equivalence increases the significance of the composition refinement relation. We also consider Dalenius leakage in the case when the marginal distributions induced by the correlation are known, giving techniques to compute stronger upper bounds in this case.