Publication
CSF 2010
Conference paper

Dynamic enforcement of knowledge-based security policies

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Abstract

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. © 2011 IEEE.

Date

27 Jun 2010

Publication

CSF 2010

Authors

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