Quantitative Information Flow, Relations and Polymorphic Types

This paper uses Shannon's information theory to give a quantitative definition of information flow in systems that transform inputs to outputs. For deterministic systems, the definition is shown to specialise to a simpler form when the information source and the known inputs jointly determine the inputs. For this special case, the definition is related to the classical security condition of non-interference and an equivalence is established between non-interference and independence of random variables. Quantitative information flow for deterministic systems is then presented in relational form. With this presentation, it is shown how relational parametricity can be used to derive upper and lower bounds on information flows through families of functions defined in the second order lambda calculus.

Automatically Adapting Source Code to Document Provenance

Being able to ask questions about the provenance of some data requires documentation on each influence on that data's existence and content. Much software exists, and is being developed, for which there is no provenance-awareness, i.e. at best, the data it outputs can be connected to its inputs, but with no record of intermediate processing. Further, where some record of processing does exist, e.g. as logs, it is not in a form easily connected with that of other processes. We would like to enable compiled software to record useful documentation without requiring prior manual adaptation. In this paper, we present an approach to adapting source code from its original form without manual manipulation, to record information on data provenance during execution.