Provenance Views for Module Privacy

Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate data items passed between module executions. However, authors/owners of workflows may wish to keep some of this information confidential. In particular, a module may be proprietary, and users should not be able to infer its behavior by seeing mappings between all data inputs and outputs. The problem we address in this paper is the following: Given a workflow, abstractly modeled by a relation R, a privacy requirement Γ and costs associated with data. The owner of the workflow decides which data (attributes) to hide, and provides the user with a view R which is the projection of R over attributes which have not been hidden. The goal is to minimize the cost of hidden data while guaranteeing that individual modules are Γ-private. We call this the Secure-View problem. We formally define the problem, study its complexity, and offer algorithmic solutions.