  Data provenance analysis has been used as an assistive measure for ensuring
system integrity. However, such techniques are typically reactive approaches to
identify the root cause of an attack in its aftermath. This is in part due to
fact that the collection of provenance metadata often results in a deluge of
information that cannot easily be queried and analyzed in real time. This paper
presents an approach for proactively reasoning about provenance metadata within
the Automatic Cryptographic Data Centric (ACDC) security architecture, a new
security infrastructure in which all data interactions are considered at a
coarse granularity, similar to the Function as a Service model. At this scale,
we have found that data interactions are manageable for the proactive
specification and evaluation of provenance policies -- constraints placed on
provenance metadata to prevent the consumption of untrusted data. This paper
provides a model for proactively evaluating provenance metadata in the ACDC
paradigm as well as a case study of an electronic voting scheme to demonstrate
the applicability of ACDC and the provenance policies needed to ensure data
integrity.
