Precise, Dynamic Information Flow for Database-Backed Applications

Abstract

We present an approach for dynamic information flow control across the application and database. Our approach reduces the amount of policy code required, yields formal guarantees across the application and database, works with existing rela- tional database implementations, and scales for realistic appli- cations. In this paper, we present a programming model that factors out information flow policies from application code and database queries, a dynamic semantics for the underlying λJDB core language, and proofs of termination-insensitive non-interference and policy compliance for the semantics. We implement these ideas in Jacqueline, a Python web frame- work, and demonstrate feasibility through three application case studies: a course manager, a health record system, and a conference management system used to run an academic workshop. We show that in comparison to traditional applica- tions with hand-coded policy checks, Jacqueline applications have 1) a smaller trusted computing base, 2) fewer lines of policy code, and 2) reasonable, often negligible, overheads.