@conference {14679,
	title = {Dynamic Enforcement of Knowledge-Based Security Policies},
	booktitle = {Computer Security Foundations Symposium (CSF), 2011 IEEE 24th},
	year = {2011},
	month = {2011/06/27/29},
	pages = {114 - 128},
	publisher = {IEEE},
	organization = {IEEE},
	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{\textquoteright}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{\textquoteright}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{\textquoteright}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.},
	keywords = {abstract 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},
	isbn = {978-1-61284-644-6},
	doi = {10.1109/CSF.2011.15},
	author = {Mardziel,P. and Magill,S. and Hicks, Michael W. and Srivatsa,M.}
}
@article {13759,
	title = {Domain-Specific Term-List Expansion Using Existing Linguistic Resources},
	year = {2002},
	month = {2002/08//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This report describes a series of experiments involving expansion of a domain-specific human-generated "seed list" using available linguistic resources. The resources used for the expansion are intended to be general purpose: two large-scale Chinese-English dictionaries and a Chinese lexical knowledge base (HowNet). The methodology involves three steps: (1) hand extraction of head words from each entry in the human-generated seed list; (2) automatic comparison of these head words against entries in the linguistic resources-where an entry matches if the head word matches the entry exactly or is included in its the semantic definition; and (3) collection of any resulting matching entries into a larger term list. The terms extracted by this process were verified manually to confirm whether they were relevant to the topic of a specific domain. An important contribution of this work is the finding that the use of a bilingual term list for the expansion proces does not provide a significant improvement over the use of a simpler, more easily produced, monoligual term list.},
	keywords = {*LEXICOGRAPHY, *LINGUISTICS, AUTOMATIC, CHINA, COMPARISON, EXTRACTION, HANDS, knowledge based systems, linguistics, resources, WORDS(LANGUAGE)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA525800},
	author = {Dorr, Bonnie J and Zhao,Tiejun}
}
