Dynamic Enforcement of Knowledge-Based Security Policies

TitleDynamic Enforcement of Knowledge-Based Security Policies
Publication TypeConference Papers
Year of Publication2011
AuthorsMardziel P, Magill S, Hicks MW, Srivatsa M
Conference NameComputer Security Foundations Symposium (CSF), 2011 IEEE 24th
Date Published2011/06/27/29
ISBN Number978-1-61284-644-6
Keywordsabstract interpretation, belief networks, belief tracking, Data models, dynamic enforcement, Facebook, information flow, knowledge based systems, knowledge-based security, knowledge-based security policy, privacy, probabilistic computation, probabilistic logic, probabilistic polyhedral domain, probabilistic polyhedron, probability, query analysis, Security, security of data, semantics, Waste materials

This paper explores the idea of knowledge-based security policies, which are used to decide whether to answer queries over secret data based on an estimation of the querier's (possibly increased) knowledge given the results. Limiting knowledge is the goal of existing information release policies that employ mechanisms such as noising, anonymization, and redaction. Knowledge-based policies are more general: they increase flexibility by not fixing the means to restrict information flow. We enforce a knowledge-based policy by explicitly tracking a model of a querier's belief about secret data, represented as a probability distribution, and denying any query that could increase knowledge above a given threshold. We implement query analysis and belief tracking via abstract interpretation using a novel probabilistic polyhedral domain, whose design permits trading off precision with performance while ensuring estimates of a querier's knowledge are sound. Experiments with our implementation show that several useful queries can be handled efficiently, and performance scales far better than would more standard implementations of probabilistic computation based on sampling.