Mediating secure information flow policies

In this paper we study secure information flow policies in the sense of Meadows [12] and others for aggregated datasets, collectively. We first present a method for combining different sensitivity levels over a common dataset and investigate its ramifications on information flow policies. Next, safe-flow policies are formulated in full generality using domain-theoretic tools, and systematically derived as closure operators from Scott continuous functions. Maximum safeflow policies correspond to the top element of the lattice of the derived closureoperator collection. We then introduce a categorical framework for information flow, in which amalgamation is used to formulate and characterize informationflow policy merging. Our methods for mediating information flow policies should be of practical interest for information sharing among multiple agencies. Our formulation of safeflow policies as closure operators from Scott continuous functions and its associated categorical formulation of safe-flow policy merging provide a sound and general theoretical foundation for the first time for this topic, setting a stage for further development in this area.