Mining Privacy Goals from Privacy Policies Using Hybridized Task Recomposition

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 f...

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Bibliographic Details
Published in:ACM transactions on software engineering and methodology 2016-08, Vol.25 (3), p.1-24
Main Authors: Bhatia, Jaspreet, Breaux, Travis D., Schaub, Florian
Format: Article
Language:English
Subjects:
Annotations
Computer programs
Human performance
Policies
Privacy
Recall
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Summary: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 used to improve recall. Lexical reuse reduces false negatives by 41%, increasing the average recall to 0.85. Last, crowd workers were able to identify and remove false positives by around 80%, which improves average precision to 0.93.
ISSN:1049-331X
1557-7392
DOI:10.1145/2907942