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Mining Privacy Goals from Privacy Policies Using Hybridized Task Recomposition by Jaspreet Bhatia; Travis D. Breaux; Florian Schaub is a Computer Science article available to read on EtoBox.

What is Mining Privacy Goals from Privacy Policies Using Hybridized Task Recomposition about?

Privacy policies describe high-level goals for corporate data practices; regulators require industries to make available conspicuous, accurate privacy policies to their customers. Consequently, software requirements must conform to those privacy policies. To help stakeholders extract privacy goals from policies, we introduce a semiautomated framework that combines crowdworker annotations, natural language typed dependency parses, and a reusable lexicon to improve goal-extraction coverage, precision, and recall. The framework evaluation consists of a five-policy corpus governing web and mobile information systems, yielding an average precision of 0.73 and recall of 0.83. The results show that no single framework element alone is sufficient to extract goals; however, the overall framework compensates for elemental limitations. Human annotators are highly adaptive at discovering annotations in new texts, but those annotations can be inconsistent and incomplete; dependency parsers lack sophisticated, tacit knowledge, but they can perform exhaustive text search for prospective requirements indicators; and while the lexicon may never completely saturate, the lexicon terms can be reliably

Who reads Mining Privacy Goals from Privacy Policies Using Hybridized Task Recomposition?

Author

Jaspreet Bhatia; Travis D. Breaux; Florian Schaub

Publisher

ACM

Published

2016

Language

EN

Field

Computer Science (Physical Sciences)