The Talent system: TEXTRACT architecture and data model

Neff, Mary S.; Byrd, Roy J.; Boguraev, Branimir

doi:10.1017/s1351324904003493

Cited by 15 publications

(15 citation statements)

References 29 publications

(30 reference statements)

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“…In particular, Text2Policy first applies shallow parsing [32] that annotates sentences with phrases, clauses, and grammatical functions of phrases, such as subject, main verb, and object. For example, the shallow-parsing component parses ACP-1 in Figure 1 as [subject: An HCP] [main verb group: should not change] [object: a patient's account.].…”

Section: Example Of Acp Extractionmentioning

confidence: 99%

“…The SPARCLE Policy Workbench [9,10,25,26] employs the shallow-parsing technique [32] to parse privacy rules and extract the elements of privacy rules based on a pre-defined syntax. These elements are then used to form policies in a structured form, so that policy authors can review it and then produce policies in a machine-readable form, such as EPAL [7] and XACML [3,33] with a privacy-policy profile.…”

Section: Related Workmentioning

confidence: 99%

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Automated extraction of security policies from natural-language software documents

Xiao

Paradkar

Thummalapenta

et al. 2012

Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering

110

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Access Control Policies (ACP) specify which principals such as users have access to which resources. Ensuring the correctness and consistency of ACPs is crucial to prevent security vulnerabilities. However, in practice, ACPs are commonly written in Natural Language (NL) and buried in large documents such as requirements documents, not amenable for automated techniques to check for correctness and consistency. It is tedious to manually extract ACPs from these NL documents and validate NL functional requirements such as use cases against ACPs for detecting inconsistencies. To address these issues, we propose an approach, called Text2Policy, to automatically extract ACPs from NL software documents and resource-access information from NL scenario-based functional requirements. We conducted three evaluations on the collected ACP sentences from publicly available sources along with use cases from both open source and proprietary projects. The results show that Text2Policy effectively identifies ACP sentences with the precision of 88.7% and the recall of 89.4%, extracts ACP rules with the accuracy of 86.3%, and extracts action steps with the accuracy of 81.9%.

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Section: Example Of Acp Extractionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automated extraction of security policies from natural-language software documents

Xiao

Paradkar

Thummalapenta

et al. 2012

Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering

110

View full text Add to dashboard Cite

show abstract

“…In order to identify the elements in a policy rule, SPARCLE uses a shallow parser [18] that is based on the IBM Unstructured Information Management Architecture [11]. A shallow parser processes text in a number of stages, beginning with operations that use limited linguistic knowledge to identify syntactic structures such as nouns, noun phrases, verbs, verb groups, and modifying phrases.…”

Section: Natural Language Parsing In Sparclementioning

confidence: 99%

“…Thus we believed that restricting users to writing rules using these common formats would be easily learned and followed. SPARCLE uses a set of grammars that operate in a cascade [18] to add meta-data tags to the rules indicating where privacy elements start and stop. In the cascade the grammars execute in a particular order where the meta-tags inserted by earlier grammars can be referenced by later grammars.…”

Section: Grammar Designmentioning

confidence: 99%

An empirical study of natural language parsing of privacy policy rules using the SPARCLE policy workbench

Brodie

Karat

2006

Proceedings of the Second Symposium on Usable Privacy and Security - SOUPS '06

111

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Today organizations do not have good ways of linking their written privacy policies with the implementation of those policies. To assist organizations in addressing this issue, our humancentered research has focused on understanding organizational privacy management needs, and, based on those needs, creating a usable and effective policy workbench called SPARCLE. SPARCLE will enable organizational users to enter policies in natural language, parse the policies to identify policy elements and then generate a machine readable (XML) version of the policy. In the future, SPARCLE will then enable mapping of policies to the organization's configuration and provide audit and compliance tools to ensure that the policy implementation operates as intended.In this paper, we present the strategies employed in the design and implementation of the natural language parsing capabilities that are part of the functional version of the SPARCLE authoring utility. We have created a set of grammars which execute on a shallow parser that are designed to identify the rule elements in privacy policy rules. We present empirical usability evaluation data from target organizational users of the SPARCLE system and highlight the parsing accuracy of the system with the organizations' privacy policies. The successful implementation of the parsing capabilities is an important step towards our goal of providing a usable and effective method for organizations to link the natural language version of privacy policies to their implementation, and subsequent verification through compliance auditing of the enforcement logs.

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“…The privacy policy is written initially in natural language. Then the workbench employs natural language shallow parsing technology [23] to parse the rules and extract the elements of the privacy rules using a defined syntax for privacy policy rules, grammars written specifically for privacy rules that reflect the defined syntax, and data dictionaries for policy domains (e.g., healthcare, banking, government). The policy rules and identified policy elements are then stored in a database.…”

Section: Research Purpose and Backgroundmentioning

confidence: 99%

Evaluating interfaces for privacy policy rule authoring

Karat¹,

Karat²,

Brodie³

et al. 2006

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Privacy policy rules are often written in organizations by a team of people in different roles. Currently, people in these roles have no technological tools to guide the creation of clear and implementable high-quality privacy policy rules. High-quality privacy rules can be the basis for verifiable automated privacy access decisions. An empirical study was conducted with 36 users who were novices in privacy policy authoring to evaluate the quality of rules created and user satisfaction with two experimental privacy authoring tools and a control condition. Results show that users presented with scenarios were able to author significantly higher quality rules using either the natural language with a privacy rule guide tool or a structured list tool as compared to an unguided natural language control condition. The significant differences in quality were found in both user self-ratings of rule quality and objective quality scores. Users ranked the two experimental tools significantly higher than the control condition. Implications of the research and future research directions are discussed.

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The Talent system: TEXTRACT architecture and data model

Abstract: We present the architecture and data model for TEXTRACT, a document analysis framework for text analysis components. The framework and components have been deployed in research and industrial environments for text analysis and text mining tasks.

Cited by 15 publications

References 29 publications

Automated extraction of security policies from natural-language software documents

Automated extraction of security policies from natural-language software documents

An empirical study of natural language parsing of privacy policy rules using the SPARCLE policy workbench

Evaluating interfaces for privacy policy rule authoring

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