Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CSF.2011.15 | Computer Security Foundations Symposium |
Keywords | Field | DocType |
belief networks,knowledge based systems,probability,security of data,belief tracking,dynamic enforcement,information flow,knowledge-based security policy,probabilistic computation,probabilistic polyhedral domain,query analysis,abstract interpretation,knowledge-based security,privacy,probabilistic polyhedron | Information flow (information theory),Data modeling,Abstract interpretation,Computer science,Knowledge-based systems,Theoretical computer science,Implementation,Security policy,Probabilistic logic,restrict | Conference |
ISSN | ISBN | Citations |
1940-1434 | 978-1-61284-644-6 | 24 |
PageRank | References | Authors |
0.92 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piotr Mardziel | 1 | 106 | 9.37 |
| Stephen Magill | 2 | 83 | 3.54 |
| Michael W. Hicks | 3 | 1061 | 61.49 |
| Mudhakar Srivatsa | 4 | 1084 | 77.97 |